The plain-English reference for humans who hire
Candidate Scoring For Dummies

The Rubric Foundation How Intelletto.ai scores a résumé in a way you can actually read — by Intelletto.ai

Learn to:
  • Read a candidate score instead of trusting one
  • Question any number, all the way back to its source
  • Survive an auditor without a forensics team
  • Tune scoring by editing a document, not the code
Scott Darrow Founder & CTO, Intelletto.ai · Manila

About This Book

For most of its life, software that scores job candidates worked like a vending machine with the buttons taped over. A résumé went in, a number came out, and nobody — not the recruiter, not the candidate, not even the regulator who's eventually going to come knocking — could see how the machine decided.

This book is about the thing we built to fix that, which we call the Rubric Foundation. You don't need to write code, you don't need a statistics degree, and you definitely don't need to read every page. Skip around. Read the cartoons. We won't be offended.

One promise up front: where something isn't finished, this book says so, right on the page. No "coming soon" fairy dust. If a number is a best guess, you'll see the words best guess.

Icons Used in This Book

Like every book in this series, the margins are littered with little pictures. Here's what they mean, so you can read the ones you want and ignore the rest:

TipA shortcut, or a way to look smart in the meeting.
WarningTouch this and something expensive goes boom.
Technical StuffFor the curious. Safe to skip — really.
RememberTie a string around your finger. This one matters.
Part I Meet the Rubric Foundation
The Nth Wave — homage — SCORING 73? ? ?
"Forty grand a year, and it still won't tell us how it got the number."

The whole thing, in one breath

Here's the deal. The old scoring software kept its math inside the program, the way a chef keeps a secret recipe inside their head. It worked. People bought it. But the moment anyone asked "why did this candidate get a 62?", the honest answer was a shrug.

The Rubric Foundation throws out the secret recipe and writes it on a card. Every number now points to a real, published authority. Every number records why it was picked. And — this is the part that makes everyone relax — a trained person can change it by editing a document, without a programmer rewriting anything.

The old way wasn't wrong. It just couldn't do the one thing the next few years of hiring are going to demand: hand someone a score they can read, challenge, and check.

Tip — the whole book in 30 seconds Old way: the recipe lives in the chef's head. New way: the recipe is written on a card anyone allowed can read — every ingredient, every measurement, and a footnote saying where each measurement came from. Same kitchen, same dishes. Now you can check the recipe. So can the candidate, the recruiter, and the regulator.

What it actually is

Strip away the jargon and a rubric is just a written document — one for each kind of job, such as "Senior Project Manager." It lists the scoring buckets, says how much each bucket is worth, names the method that reads each bucket, and keeps a shelf of real-world authorities that every number points back to.

Every single number in that document — every weight, every cutoff, every points value — is wrapped in a little tag carrying its source, how confident we are, who set it, when, and the reason. At scoring time the engine reads the rubric, hands each bucket to its named method, takes a photo of the whole thing next to the evidence and the citations, and files it. Three ideas are hiding in there. Here they are inside one real number.

The recipe card · hard-skills weight, Senior PM rubric
value: 25.0          # the number itself
calibration_status: CALIBRATING_SP3_LIVE
calibration_authority: intelletto_curator_team
calibration_effective: 2026-06-10
framework_anchors: [ SH-1998, ONET-13-1082.00 ]
numeric_rationale: >
  Hard-skills weight set at 25% from the Schmidt & Hunter
  1998 meta-analysis: structured assessment of job knowledge
  + skill explains ~26% of supervisor-rated performance for
  professional roles. SP3's calibration loop is now live and
  this number is being checked against our own outcome data;
  its badge flips to "calibrated" once enough decisions clear
  the bar.
The recipeThe value and where it sits — which bucket, how much weight. A curator changes it by editing this line.
The citationThe framework_anchors point at the outside world — a 1998 selection-science study and an O*NET code. No anchor, no publish.
The honesty labelThe calibration_status says, out loud, how sure we are. This one's a grounded estimate now under live calibration — and it says so, instead of pretending it's already proven.
Technical Stuff — skip if you like "SH-1998" is shorthand for Schmidt & Hunter (1998), a famous meta-analysis of what actually predicts job performance. "ONET-13-1082.00" is the government occupation code for project-management specialists. You don't need to memorize either. You just need to know they're real, public, and that a curator can't publish a number unless its anchor resolves to something like them.
Part II The Citations and the Honesty Label
The Nth Wave — homage — PENDING
"Around here we don't fake confidence, Stan. We stamp it and move on."

What every number points at

The list of authorities isn't decoration — it's a bouncer. A curator cannot publish a number whose source doesn't resolve to a recognized framework. If a citation is left dangling, the document refuses to publish, full stop. Here's the shelf a rubric can pull from today, so the next time someone asks "where did this score come from?" the answer is one piece of paper instead of a week of detective work:

Count them up and you've got the twelve external frameworks every number leans on. None of them is ours. That's the point — we don't get to grade our own homework.

The part that surprises people: the honesty label

The single feature most likely to make a reader sit up is that every number tells you how sure we are of it. There are exactly three levels of sureness, and each one makes a different promise. The old method had nothing like this — a hardcoded weight came with no little flag saying whether it was nailed down by science or just an engineer's Tuesday-afternoon guess.

Framework-fixed
Locked by an outside authority
We don't get a vote — the value is set by a published standard. Think the six CEFR language levels, or the EEOC 80% line.
Status — in use today
Pending validation
A grounded best guess
A curator set it from the research, and SP3's loop is now testing it against our own real hiring outcomes. This badge is the honesty signal — and it's now a moving target, not a permanent one.
Status — most numbers today, and shrinking
Calibrated
Proven against real outcomes
Checked against our own outcome data with a defined statistical procedure. SP3 — the stage built to produce this — is now live, so numbers begin earning this badge as the outcomes behind them accumulate.
Status — SP3 calibrating now
Warning — don't oversell this SP3 being live is not the same as "every number is validated." Calibration now runs continuously, but a number only earns the "calibrated" badge once enough real outcomes sit behind it. Until a number's badge says calibrated, it's still a grounded best guess — so if a salesperson tells a buyer a number is "proven against outcomes" before its badge agrees, they're wrong, and the rubric will happily contradict them in writing.

"A score you can't question is a score you have to take on faith. The whole point of the foundation is to get rid of the faith."

Three pillars, and why we built all three at once

The Rubric Foundation isn't a bet on one clever idea. It's a bet that three different pressures all show up in the same eighteen-to-thirty-six-month window — and that paying once for a citation-backed scoring base answers all three. Each pillar on its own is arguable. Stacked together, they're the right shape for a company that plans to keep selling in regulated markets.

Pillar 1
Surviving the regulators
Scoring becomes explainable and auditable, per customer. When a regulator asks how someone was scored, the answer is one file — not a forensic dig. Anchors: GDPR Art 22, the EU AI Act, NYC Local Law 144, the EEOC four-fifths rule, the UK Equality Act, Singapore PDPA, Philippines DOLE.
Honest tradeoffNo regulator has actually asked us for a packet yet. This bets on the next 18–36 months, not on demand today.
Pillar 2
Earning candidate trust
Per-candidate evidence and per-bucket explanation make it possible to give candidates real rights: to see what drove their score, challenge a piece of evidence, ask for a rescore, and appeal. A black-box integer supports none of that. A readable rubric does.
Honest tradeoffThe candidate portal that uses this is now built (SP5) — and it brings data-protection duties the old method never had.
Pillar 3
Fixing it without a fire drill
Scoring rules become a document a curator edits — live in minutes — instead of code an engineer rewrites, reviews, and redeploys over days. A real April 2026 incident, where every job defaulted to a hard-skill score of 60, cost a senior engineer six hours plus a deploy. Under the rubric, it's a one-line edit.
Honest tradeoffThe payoff only starts once a curator team exists. That headcount is zero today, so we pay both costs at once.
Part III The Chain: What Unlocks What
The Nth Wave — homage — SP1 SP2 SP3 SP4 SP5 SP6 SP7
"Frank. FRANK. Step away from SP1."

Seven sub-projects, one falling row of dominoes

The foundation was the first of seven sub-projects, named SP1 through SP7. They're not seven separate features you can pick off a menu — they're a chain, where each link unlocked the next as it landed. That chain is now built end to end. Understand the chain and you understand the whole thing, including what each link does and which still leans on data, not code, to finish maturing.

SP1 → SP7 · same pipeline, seven faces
SP1Live
Rubric Foundation
SP2Built
Outcome Capture
SP3Built
Empirical Calibration
SP4Built
Bias / Disparate Impact
SP5Built
Candidate Portal
SP6Built
Candidate AI Assistant
SP7Built
Recruiter Explanation

Each link leans on the one before it. Read top to bottom, the chain shows what each stage does now that all seven are built — and the one thing that still finishes on data, not on a deploy:

Remember The order wasn't bureaucracy — it was physics. You can't calibrate against outcomes (SP3) before you've recorded any (SP2); you can't let a candidate challenge a score (SP5) before the score carries per-candidate evidence (SP1). The row fell in that order, and it stayed standing because SP1 held.
Part IV What's Built, What's Still a Gap
The Nth Wave — homage — HIRING: curators (0)
"The curator team will now come to order. All zero of us."

No "coming soon." Here's the real status board.

Every Dummies book has the table you actually flip to. This is ours. Green means it's built and running. Yellow means it's wired but still maturing on data. White means designed but not yet built — and after this update, nothing sits there anymore.

Piece Status What it actually is
Rubric & citation schema Live In the production database; every scorecard can point at a rubric and its citation chain.
8 typed scoring methods Live Skills, education, language, domain terms, scope, tenure, culture-fit, compliance.
Extraction normalizer Live Turns real résumé data into the shape the methods expect.
Two seeded rubrics Live A Senior Project Manager rubric (O*NET-SOC 13-1082.00) and a general fallback exist as real production rows.
Job role-family tagging Live Backfilled tenant-wide on 13 June; the pilot tenant now resolves most job descriptions to their specific rubric, with the general fallback as the safety net.
Calibrated numbers Calibrating SP2 capture and SP3 calibration are now built and running; numbers move from pending to calibrated as outcomes clear the bar. None are stamped calibrated until that bar is met.
Curator team Headcount 0 The CTO maintains the rubrics today, so the maintainability payoff hasn't started yet. (See the cartoon above. That's not entirely a joke.)
SP2–SP7 builds Built All six are built — Outcome Capture, Empirical Calibration, Bias monitor, Candidate Portal, Candidate AI Assistant, and Recruiter Explanation. What's left now finishes on data, not on a deploy: SP3 needs outcome volume to calibrate.

What actually changes on your desk

The neat part of the design: recruiters, candidates, and operators all look at the same explanation — just from their own side of the glass.

Recruiter
Same buttons, deeper "why"
Your pools, longlists, rankings, and decisions — advance, reject, interview, pass — work exactly as before. What's new: open a score and you see the rubric breakdown — each bucket's weight, the evidence, the citation behind every number. The black box grows a glass front.
Candidate
A score you can argue with
With the portal now built, you can see your own scorecard, trace which line on your résumé drove each bucket, and challenge a specific piece of evidence or ask for a rescore — explained for your jurisdiction's rules.
Operator
Edit a document, not code
Onboarding a customer is a deliberate sequence: seed the rubrics, run in shadow, compare to legacy, then flip the switch. Tuning scoring becomes a curator editing a document that ships in minutes — once the curator team exists.

"One investment, three loads, one shared explanation — put together honestly, with the gaps printed on the page instead of hidden behind the score."

The Part of Tens

No book in this series is finished without it. Ten things to remember about the Rubric Foundation, in case you skipped everything else and flipped straight to the back (we see you):

  1. The recipe is on a card now. The scoring math is a readable document, not a secret in the code.
  2. Every number cites a real source. No anchor, no publish — twelve external frameworks back the math.
  3. Eight typed methods do the scoring, and on production they're failing 0 times out of 416.
  4. Every number wears an honesty label. Framework-fixed, pending, or calibrated — and it says which.
  5. Calibration is now running. SP3 is live; numbers move from pending to calibrated as real outcomes clear the bar, and none claim "proven" before their badge says so.
  6. Three pillars, one base. Regulators, candidate trust, and maintainability — paid for once.
  7. A skeptic has to be right three times to make the bet wrong; a believer needs one payout to break even.
  8. SP1 through SP7 is a chain — and it's now built end to end. Each link landed in order; what's left finishes on data, not on a deploy.
  9. The dev tenant is live on the rubric path, running about ten points below legacy (median −11.32) while it calibrates.
  10. The curator team's headcount is zero. Until that changes, the CTO is the curator, and the maintainability payoff hasn't started.
SD
About the Author — Scott Darrow

Founder & CTO of Intelletto.ai, an AI-native Human Capital Intelligence Platform based in Manila. Thirty-five years building platforms that scale and survive across fintech, BPO, enterprise, and digital health. Believes the gaps belong on the page. Reachable at intelletto.ai and darrow.me.

White paper · Rubric Foundation · derived from RUBRIC-FOUNDATION-RATIONALE. Presented in the plain-English reference style as an affectionate homage to the classic how-to book — original cartoons drawn for this document. Not affiliated with, endorsed by, or produced by the …For Dummies® series or its publisher; "For Dummies" is a registered trademark of its respective owner.