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Record W2120169586 · doi:10.7202/1025781ar

On Writing Notes in the Field: Interrogating positionality, emotion, participation and ethics

2014· article· en· W2120169586 on OpenAlexaffvenue
Jennifer Thompson

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsMcGill University
Fundersnot available
KeywordsFieldnotesField (mathematics)SociologyField researchAnthropologyEthnography

Abstract

fetched live from OpenAlex

Fieldnotes help researchers document research activities and position themselves in the field, invariably constructing the research, the researcher and the knowledges produced. Yet the process of how fieldnotes are produced often remains invisible. These “Notes from the Field” explore one doctoral student’s experiences writing fieldnotes. Interrogated here are some of the tensions that emerged writing fieldnotes in relation to positionality and emotion, as well as regarding participation and ethics.

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)0.027
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.088
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0150.023
Scholarly communication0.0110.011
Open science0.0020.010
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.712
GPT teacher head0.635
Teacher spread0.077 · 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
GenreMethods

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

Citations14
Published2014
Admission routes2
Has abstractyes

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