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Informing Traces

2010· book-chapter· en· W2506728089 on OpenAlexaff
Pamela J. McKenzie

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsWestern University
Fundersnot available
KeywordsBracketing (phenomenology)Set (abstract data type)Relation (database)Work (physics)Public relationsComputer scienceKnowledge managementSociologyPolitical scienceData scienceEpistemologyEngineeringData mining

Abstract

fetched live from OpenAlex

The concept of “traces” is useful for understanding the collaborative practices of informing. Readers of documents leave traces of their use, and institutional talk embeds traces of collaborative work, including work done and elsewhere and at other times. This chapter employs a multifaceted qualitative strategy of analytic bracketing to analyze traces in midwives’ and clients’ discussions of clinical results. Results are used to identify and evaluate trends in relation to the current case or to universal norms. Conflicting forms of evidence may need to be negotiated. Barriers may arise when results or sources are inadequate or unavailable. Midwives and women manage these barriers by flexibly assigning the role of information provider in official and unofficial ways. The analysis of traces provides insight into the hows and whats of collaborative work and reveals it to be a complex set of practices that go well beyond the immediately visible contributions of others.

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)0.017
metaresearch head score (Gemma)0.038
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0070.041
Scholarly communication0.0180.040
Open science0.0030.017
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0240.005

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 head0.024
GPT teacher head0.313
Teacher spread0.289 · 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
GenreOther

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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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