'Organization Text Work' and ISO 26000: Influencing the Meaning of a Practice-Defining Standard
Bibliographic record
Abstract
Drawing from a qualitative and quantitative analysis of texts documenting the five-year negotiation of ISO 26000: Guidance on Social Responsibility we develop and test a model of ‘organizational text work’ that predicts whether the behaviors and language used by organization(s) in developing and disseminating texts, with the purpose of altering the meaning of a practice-defining standard, will lead other organizational actors to accept them. Counter intuitively, our findings suggest that an organization’s use of ethos narratives, derived from institutionalized beliefs, norms, and rules, in the language of texts as well as coalitions with other organizations to create texts can lead to a backlash from other actors as their texts are less likely to be accepted over time. Instead our results suggest that organizations that rely upon logos based reason within the narratives of texts and signal internal cohesion through their dissemination are more likely to have their texts accepted.
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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 | 0.028 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".