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Record W2565501307

Preface: Functional Research

2016· article· en· W2565501307 on OpenAlexaffabout
Philip McShane

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEvolution and Science Education
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

This is the first of what was originally conceived to be twenty-five special volumes of the Journal of Macrodynamic Analysis.The volumes were to be a distinctive unit, the distinctiveness being related to the efficiency of the new science that is being advocated by this endeavor.The original plan was ambitious and indeed strained the capacity of those involved in the project.It would be quite foolish of me to attempt any compact presentation here of either the science or the novelty of its anticipated efficiency. 1 The presentation 2 would have been effectively seeded by the twenty-five volumes, and the external efficiency grounded through the programs generated by the contents of the eighth, sixteenth, twenty-fourth and twenty-fifth volumes in the series.3 As it stands the scaled-back project will produce four volumes.The first volume is my own effort to in functional research.Subsequent volumes will reveal the fledgling

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.172
Threshold uncertainty score0.574

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0040.005
Scholarly communication0.0070.008
Open science0.0020.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.1720.070

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 head0.061
GPT teacher head0.268
Teacher spread0.208 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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