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Record W4385424703 · doi:10.1075/tris.22007.gag

Multiple constraints, multiple avenues

2023· article· en· W4385424703 on OpenAlexaff
Anne-Marie Gagné

Bibliographic record

VenueTranslation in Society · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAgency (philosophy)Product (mathematics)Power (physics)Structure and agencySociologyEpistemologySocial psychologyPsychologyPhilosophySocial scienceMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.113
GPT teacher head0.297
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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