Exposure, access, and inequities: Central themes, emerging trends, and key gaps in Canadian environmental justice literature from 2006 to 2017
Bibliographic record
Abstract
Environmental injustices—in the form of inequitable distribution of environmental risks and benefits, uneven access to decision‐making processes, and misrecognition of communities—have been documented globally. However, in Canada, many have argued that the story of environmental injustice is less widely known, with more fragmented research that has produced little in terms of public policies intended to alleviate injustice. This paper uses a meta‐narrative review approach to map the evolution of environmental justice research in Canada between 2006‐2017, and characterize how central themes, emerging trends, and gaps in the literature have changed since the last review of this kind was completed. We conducted a systematic search of publications addressing environmental justice in Canada, yielding 820 publications. We coded abstracts to assess patterns of coverage across space, time, topics, and populations of focus. We find that Canadian environmental justice literature has continued to grow in quantity and scope, addressing more dimensions of environmental harms and benefits, and from an increasingly integrated perspective. However, there remain important and persistent gaps in its coverage. Future research that more fully addresses these geographic (e.g., Atlantic and Prairie regions), topical (e.g., focus on prevention), and recognitional (e.g., racialization) gaps is needed to inform policy‐making and promote justice.
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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.085 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".