Mandatory Disclosures as Calculative Spaces: Public Sector Accountability on Restoring Species at Risk*
Bibliographic record
Abstract
Abstract While sustainability has been a subject of growing inquiry in the accounting literature, biodiversity loss, an issue of critical societal importance, has not received the same attention. Furthermore, despite corporations, nongovernmental organizations, and the public sector all having distinct influences on biodiversity loss, the role of the public sector remains largely unexamined. Mobilizing Cuckston's (2022) framework for analyzing disclosures as calculative spaces, our study examines the framing of public sector reporting on biodiversity conservation. Specifically, through discourse analysis, we scrutinize the mandatory annual reports prepared by the Canadian federal government under the 2002 Species at Risk Act (SARA), which is aimed at preventing species extinction. The disclosures within these reports present discussions on the government's administration of SARA, thereby offering insights into the government's efforts to discharge accountability over the protection of species at risk. Our findings examine the significance of public sector reporting in enabling accountability for species conservation and discuss the ways in which mandatory disclosures contribute to this process.
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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 | 0.041 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".