My Long Journey Home: How the Acadian Exile Shaped My Life as a Therapist
Bibliographic record
Abstract
Although attachment patterns and early relational interactions are very important in creating implicit and procedural memories, I contend, along with many authors (Gentile, 2010; Sucharov, 2012), that the historical, cultural, political, and social contexts give them meanings. Grounding myself in a relational systems theory of trauma and therapeutic healing (Brothers, 2008), I attempt to capture how a historic traumatic event in my ancestors’ lives, the Acadian Deportation, has shaped and affected my life and work as a therapist. Different themes and meanings—for example, submission and surrender (Ghent, 1990), autonomy and liberation, and restorative efforts in the aftermath of trauma that involve the reduction of complexity (Brothers, 2008)—will be revealed as part of the legacy of the historical traumatic exile and return of my Acadian ancestors. After briefly describing the tragic history of the Acadian people, I retrace my own initiatory, and never completed, journey back home as an Acadian woman therapist, through traumatic submission and its active counterpart, the impulse to dominate. A brief vignette from my work with a patient serves to illustrate how my Acadian heritage is still an active and conflictual process for me.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".