True selves, suspicious lives: Public deceits, hopes of restoration, and existential troubles in misdocumented pasts
Bibliographic record
Abstract
How do individuals, after having discovered they were lied to about the conditions of their births and their childhoods, seek out their own identities and re/establish the “truths” about themselves? Based on two ethnographic studies conducted in sites where lives and kinships were disrupted by political violence, this article aims to examine the urge for narrative coherence in contexts defined by public deceit and betrayal. In Argentina, [Author 1] lived with the nietos who, decades after the dictatorship, discovered they had been stolen and educated by those responsible for their parents’ death. In Ethiopia, [Author 2] met with adopted children who were searching for their life “of before”. In these two contexts, the interviewees explained how their lives had been shattered when they discovered the lies they had been told. Their testimony equally revealed how they felt an existential and urgent need to re-establish the “truth”. Drawing on their experiences and their feelings, this article examines the link between two truths, truth regarding the past and truth about oneself, and explores the need to be certain of facts in the making of identities.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.066 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".