Bibliographic record
Abstract
Numerous research studies have explored how institutions such as schools are produced as white spaces. Whiteness is a socio‐spatial process that constitutes particular bodies as possessing the normative, ordinary power to enjoy social privilege. Within the Canadian colonial context, whiteness has been produced historically through the violent confiscation of land and resources from Indigenous Peoples. This violence has been silenced through grand narratives of Canadian “tolerance,” and white‐settler fantasies of the Canadian landscape as empty and wild. Many environmental education programs continue to rely upon and reproduce these colonial ideas of race and space. Escaping the classroom, Canadian environmental education programs propose to advance personal and educational decolonization through experiential land‐based learning. Integrating the discussions in anti‐racist, anti‐colonial education with the literature on race and nature, this qualitative article draws from student interviews and artefacts to interrogate how whiteness continues to be normalized within environmental education through various dominant narratives of Canadian nation building, such as: the disaffiliation of whiteness from the violence of colonialism, reifying Canadianness as goodness and innocence; the ongoing erasure of Indigenous Peoples and histories from the land; and the reification of wilderness as an essentialized, empty space. These narratives continue to entitle white people to occupy and claim originary status in Canada, signifying wilderness and the environment as a white space.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.031 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 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".