Bibliographic record
Abstract
Any organization, to fulfill its mandate from the society, needs to have the legitimacy to use collective resources. Conferred almost automatically at the birth of the organization, it has to be maintained and even repaired when necessary. Legitimacy appears then as a conversation between the organization and the general public. Noticeably, this continuous conversation is sustained through the media and also through documents issued by the firm, particularly the annual report. The firms use discursive strategies to entertain their legitimacy. Using semiotic analysis in the frame of a multiple cases study (6 firms over 5 years), this paper isolates the different stories in the annual reports, including the images that are integrated parts of these narrations. We apply the semiotic instrument to these stories to deconstruct the content and expose the actor filling actantial roles. We found a substantial amount of stories (187 in 30 reports) containing the categories developed by Greimas & Bremond from the work of Propp, implying an intensive use of the report in the conversation maintaining legitimacy.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.067 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".