Corporate governance research on the free web: a selected annotated guide
Bibliographic record
Abstract
Purpose The web offers a large and ever expanding range of information sources on the popular and widely researched topic of corporate governance. This paper aims to introduce keys sites of quality and relevance to those interested in researching the field of corporate governance using freely available web resources. It will also aims to prove useful to librarians who wish to develop web‐based subject pathfinders in this field or who want simply to connect with and build their knowledge of major topics and participants in the field of corporate governance. Design/methodology/approach By way of introduction important or groundbreaking works in the corporate governance literature are identified and cited in the paper to place selected web sites within the context of recent and historic developments in the area of corporate governance. A wide range of web‐based sources were consulted and critically evaluated in the study. Findings The result of this work is a significant sampling of quality web‐based information sources with evaluative annotations. Originality/value Given the recent explosion in information resources available on the topic of corporate governance, this paper will prove especially timely and useful to anyone interested in accessing and interacting with quality information on the free web from a wide range of significant players and stakeholders in the corporate governance arena.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".