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Record W4405944556 · doi:10.1007/979-8-8688-1061-9_7

Reassess with Gen AI

2024· book-chapter· en· W4405944556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDesign Thinking · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEthics and Social Impacts of AI
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePsychology

Abstract

fetched live from OpenAlex

Once upon a time in ancient Greece, Diogenes was known for his unconventional wisdom. One day, a curious student asked him, "Master, how do you know when you have truly learned something?" Diogenes, with his typical mischievous grin, replied, "Come, let's visit the marketplace." They walked through the bustling market until they reached a potter's stall. Diogenes picked up a clay cup and handed it to the student. "What is this?" he asked. "A cup," the student replied. "Indeed," Diogenes said, "but what if it is cracked? Would it still hold water?" "No, Master," the student answered. Diogenes then took the cup, filled it with water, and to the student's surprise, it leaked. "Knowledge is like this cup," Diogenes said. "To know if you’ve learned something, you must test it. If it holds, you’ve learned. If it leaks, you must learn more." The student pondered this and asked, "But how do I test my knowledge?" "By using it," Diogenes replied. "Teach others, apply it in real situations, and reflect on your experiences. Evaluate your success and failures. Over time, you’ll know you’ve learned when your knowledge holds up under pressure, like a cup that doesn’t leak."

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.346
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.354
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it