Bibliographic record
Abstract
Highlighting the importance of told, untold, and forgotten stories both in our own history and in previous generations is one of the central themes in French’s chapter. She links this to her personal story of returning on her own at the age of 18 to Canada, her birth country, for university where she spent her childhood before her family moved to London. Parallels are drawn between the search for a place of belonging and the immigration stories of both her maternal and paternal grandparents. The social, emotional, and psychological complexities resulting when intergenerational trauma, often shame-based, remains hidden is discussed with references to some of the contemporary literature on the subject. Ideas explored include whether we can fully make sense of the past when much of a family’s history cannot be traced as well as the challenges implicit in ‘finding our place’ without such narratives to orientate us. Ending with a clinical vignette, French illustrates how the themes she discusses feature in psychotherapy sessions with adolescents in inner city Sixth Form colleges where the student population is culturally and socio-economically diverse.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".