Beyond sequential presentation: Misconceptions and misrepresentations of sequential lineups
Bibliographic record
Abstract
Malpass, Tredoux, and McQuiston-Surrett (2009), hereinafter 'MTM', provide comments \non the sequential lineup, research comparing sequential and simultaneous lineups, and \nthe policy implications of this literature. We will comment on points of agreement and \ndisagreement. First, we agree with the following: \n(1) Peer review, publication of results, and diversity of methods, procedures, and \nsubject populations significantly contribute to the value of research as a basis both \nfor psychological understanding and for recommended policy. \n(2) Absence of error, omission, and confounds make interpretation and application \neasier. \nThese conclusions are not revolutionary but seem to occupy a great deal of MTM's \nthinking. \nWe disagree with many things that MTM have to say but have room here only to \naddress a few.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".