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Record W2108508706 · doi:10.1177/030631201031004003

`Creative Solutions' and `Fibbing Results'

2001· article· en· W2108508706 on OpenAlexaff
Wolff‐Michael Roth, G. Michael Bowen

Bibliographic record

VenueSocial Studies of Science · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsLakehead UniversityUniversity of Victoria
Fundersnot available
KeywordsEnculturationIndeterminacy (philosophy)Field (mathematics)EpistemologyConstraint (computer-aided design)SociologyComputer scienceEcologyMathematicsPhilosophyAnthropology

Abstract

fetched live from OpenAlex

This paper is concerned with enculturation in field ecology, where students are frequently required to spend long periods of time in the field with little or no contact with others. We document the practical choices that allow aspiring ecologists, more or less successfully (in their own accounts), to deal with the indeterminacy and open-endedness of independent fieldwork, involving a version of what Collins called `the experimenters' regress'. `Constraint satisfaction' is a suitable concept to describe how our participant dealt with this regress. However, `creative solutions' and `fibbing results' become viable options when problems are deemed insurmountable.

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)0.053
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.111
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0050.039
Scholarly communication0.0100.014
Open science0.0040.013
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0190.003

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 head0.180
GPT teacher head0.460
Teacher spread0.279 · 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.

Study designQualitative
DomainMethods
GenreEmpirical

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

Citations54
Published2001
Admission routes1
Has abstractyes

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