Bibliographic record
Abstract
Abstract The abstract nature of human agency, as a capacity or a possibility to exert power, complicates its empirical examination. While translated and revised texts represent the cumulative result of the agents’ decisions, they offer limited information on their motivation, goals and sense of agency. In this paper, we employ a multi-method approach to examine the revised edition ( Galeano rev. Sánchez 2013 ) of Mémoire du feu ( Galeano transl. Couffon 1985 ; transl. Binard 1985, 1988), the French translation of Memoria del Fuego ( Galeano 1982 , 1984 , 1986 ). Through a dialogue between semi-structured interviews (focused on the agent’s choices, motivations and sense of agency) and comparative textual analysis (focused on the result of her decisions), we investigate why Mémoire du feu was revised and how this project unfolded. The interaction taking place during semi-structured interviews allowed for the exploration of some dimensions of the reviser’s work that would otherwise have remained unarticulated discursively and helped us understand the textual results of her decisions. The revision appears as an agent-based process, as constraints were negotiated by the reviser, and resources were selected by her. Finally, the reviser’s sense of agency, widened by her multipositionality, emerged as a crucial factor in shaping the textual product.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".