Bibliographic record
Abstract
Purpose This paper aims to analyze so‐called sustainability, corporate social responsibility or citizenship reports, as artefacts of a compromise between an institutional entrepreneur (IE), the Global Reporting Initiative (GRI), and companies. Some companies take on this invitation but to which extent the information they produce as a result corresponds to the ideal promoted by the IE? Design/methodology/approach A sample of ten reports from Canadian companies were analyzed using a combination of deductive and inductive coding techniques. The discourse and pictures were analyzed to identify whether they represent path creation (adherence to the sustainability ideal) or path dependence (the expression of traditional business interests and practices). Findings The study findings show that companies adopt the sustainability reporting guideline and ideal promoted by IE, but only partially. Path dependence and path creation are in tension, a condition typical of innovative processes according to the actor network theory (ANT) framework. It suggests that the market for sustainability information is under construction. Originality/value The value of the paper is that it examines voluntary disclosure of social and environmental performance by companies, using the notion of IE from the neo‐institutionalist theory, as well as the innovation model from the ANT. The originality of the paper also lies in its methodology – particularly the use of a mixed method—including the composition of “poems” with “verses” extracted from the corporate reports.
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 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".