Bibliographic record
Abstract
This chapter is a reflection on the author's work in the context of trauma-focused qualitative research entitled “Risk and protective factors for the mental health consequences of childhood political trauma (Argentina 1976-1983) among adult Jewish Argentinian immigrants to Israel.” By examining the author's emotional reactions during the process of the data collection and analysis of her doctoral study, the author will explore the challenges that she faced, as well as the solutions she employed (both the effective and ineffective). More specifically, using the lens of the psychoanalytical term “countertransference”, she will discuss the manifestations of her positionality as a qualitative researcher and its impact on her engagement with her study. The author will elaborate on different strategies that she used for her study, and propose qualitative researchers to use “countertransference” as a way to understand and address the complexity of a researcher's positionality in narrative research.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".