Obscuring the speaker’s stance: when explicitating results in implicitation
Bibliographic record
Abstract
The paradox that lies at the heart of the phenomenon of explicitation, taken as a broad category, is that explicitation on one level of analysis can correspond to implicitation on another. Some explicitations add or change linguistic elements to clarify the original text, others serve to reinforce the original speaker’s attitude; however, clarifying the text might in fact affect its global meaning, in particular when the text’s intention is precisely to remain obscure. In these cases, from a semantic or syntactic point of view, such translational shifts are explicitations, but on a deeper level of meaning, they can be considered implicitations, since they obscure the speaker’s stance, thus making the global meaning of the text more implicit. We thus advocate studying narratological explicitness from the angle of the more specific phenomenon of “reduction of complex narrative voices” (Chesterman 2010: 41).
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".