The Role of Analogy in the Institutionalization of Sustainability Reporting
Bibliographic record
Abstract
We study institutional entrepreneurship in an emergent field by analyzing the case of the Global Reporting Initiative (GRI) and its efforts to purposefully institutionalize the practice of sustainability reporting. We suggest that analogies affect institutionalization processes through two mechanisms. In the early stages of institutionalization, analogy operates primarily as a normative mechanism, and adoption is driven mainly by an instrumental logic. This emphasis on similarity to existing institutions stresses conformity and promotes legitimacy. Yet analogies can also have a cognitive effect on institutional design, especially once initial acceptance from the environment has been secured, by directing attention toward incongruences between the emergent institution and its analogical source. Institutional entrepreneurship can spur innovation and departure from existing institutions by highlighting limitations of the analogical source and providing a compelling value-rational argument that underscores the worth of the new institution. This theoretical contribution helps explain how analogies to existing institutional practices can both provide legitimacy to novel institutions and constitute the basis for a creative process of institutional design.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".