Indigenous peoples, environmental accountability and the semantic meaning of resource extraction firm disclosures
Bibliographic record
Abstract
Purpose The study aims to explore how Indigenous peoples and their concerns become “entextualized” within the environmental disclosures of resource extraction firms. Design/methodology/approach A mixed-methods content analysis of 11,850 annual information forms filed by resource extraction firms with Canadian security regulators between 1997 and 2023 is conducted. FinBERT transformer encodings, agglomerative hierarchical clustering and computer-assisted techniques are combined with inductive analyses. Findings The findings show that, although Indigenous peoples and their concerns have become a more important element in environmental disclosures, dominant semantic meanings tend to view Indigenous people as impediments. At the same time, the entextualizations of Indigenous peoples and their concerns sometimes escape these dominant frames. Big firms appear to be no more likely to exhibit leadership or substantively take Indigenous peoples and their concerns into account than smaller firms. Originality/value The study offers a longitudinal perspective on how the environment and Indigenous peoples are portrayed in corporate disclosures. The study emphasizes the need to view environmental accountability as inextricably intertwined with accountability to Indigenous peoples and also illustrates the importance of identifying the semantic meanings that are being communicated. We propose that analyzing how and why specific semantic meanings about Indigenous peoples and their concerns become entextualized provides activists and policy-makers with a starting point for improved disclosure practices and, hopefully, better resource extraction practices.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".