Exploring Perspectives in Narrative Research: An Indonesian Case Study*
Bibliographic record
Abstract
Cet article veut examiner le processus par lequel deux chercheures, situées à des endroits très différents, analysent les données d'une étude qu'elles ont menée ensemble. À partir d'un projet de recherche narrative dans un groupe de collaboration, soutenus par un groupe central du programme d'études supérieures en études de la condition féminine à L'Université d'Indonésie (programme Kajian Wanita), les rôles de la chercheure canadienne et de la chercheure indonésienne étaient d'une importance cruciale à la fois dans la conceptualisation et dans L'analyse des données. Les auteures explorent ici quelques‐unes des façons par lesquelles leur position différente influe à la fois sur leur relation en tant que chercheures et sur leur analyse des données. This paper is an attempt to examine the process whereby two researchers, situated very differently, analysed the data arising from a jointly conducted study. The roles of the Canadian and the Indonesian researcher were crucially important in both framing and analysing the data arising from a collaborative group narrative research project, carried out with a core group in the graduate program in Women's Studies at the University of Indonesia (Program Studi Kajian Wanita). In this paper we explore some of the ways in which our different positioning affected both our relationship as researchers and our analyses of the data.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".