"I was… Until… Since then…": Exploring the Mechanisms of Selection in a Tragic Narrative
Bibliographic record
Abstract
This paper presents a reading of Amos’s life story that follows an interpretive model based on mechanisms of narrative selection (Spector-Mersel, 2011). Drawing on the notion of the narrative paradigm, this model is derived from narrative epistemology, and specifically from a conceptualization of how identities are claimed through stories. The narrative production is conceived of as consisting of six mechanisms of selection, through which biographical facts are sorted, with the purpose of confirming an end point. Accordingly, the analysis seeks to identify the expressions of these mechanisms in the story, as a means to recognize the identity being claimed. The examination of the mechanisms of selection displayed in both the content and the form of Amos’s story reveals a split end point, which divides Amos’s life into “before” and “after” the stroke. The “I was” part strictly corresponds with the cultural ideal of a Sabra-Kibbutz member, depicting Amos’s prior self as a “culturally appropriate” and highly significant figure in his collective. In contrast, the “since then” part, which portrays the past 15 years since the stroke as an extended present, conveys his being “outside” of both the culture and the collective. Considering Amos’s story a clear instance of a tragic narrative, some insights are offered that can shed light on possible manifestations of this story genre.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".