MétaCan
Menu
Back to cohort

Quasi-Replication and the Contract of Error: Lessons from Sex Ratios, Heritabilities and Fluctuating Asymmetry

2000· article· en· W2123644333 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

VenueAnnual Review of Ecology and Systematics · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsBamfield Marine Sciences CentreUniversity of Alberta
Fundersnot available
KeywordsReplication (statistics)FunnelIncentiveComputer scienceEconometricsPositive economicsStatisticsEconomicsMicroeconomicsMathematics

Abstract

fetched live from OpenAlex

▪ Abstract Selective reporting—e.g., the preferential publication of results that are statistically significant, or consistent with theory or expectation—presents a challenge to meta-analysis and seriously undermines the quest for generalizations. Funnel graphs (scatterplots of effect size vs. sample size) help reveal the extent of selective reporting. They also allow the strength of biological effects to be judged easily, and they reaffirm the value of graphical presentations of data over statistical summaries. Funnel graphs of published results, including: (a) sex-ratio variation in birds, (b) field estimates of heritabilities, and (c) relations between fluctuating asymmetry and individual attractiveness or fitness, suggest selective reporting is widespread and raise doubts about the true magnitude of these phenomena. Quasireplication—the “replication” of previous studies using different species or systems—has almost completely supplanted replicative research in ecology and evolution. Without incentives for formal replicative studies, which could come from changes to editorial policies, graduate training programs, and research funding priorities, the contract of error will continue to thwart attempts at robust generalizations. “For as knowledges are now delivered, there is a kind of contract of error between the deliverer and the receiver: for he that delivereth knowledge desireth to deliver it in such a form as may be best believed, and not as may be best examined; and he that receiveth knowledge desireth rather present satisfaction than expectant inquiry; and so rather not to doubt than not to err: glory making the author not to lay open his weakness, and sloth making the disciple not to know his strength.” The Advancement of Learning, Francis Bacon, 1605 ( 8 :170–171)

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.098

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.283
Teacher spread0.239 · 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