Bibliographic record
Abstract
As policies to conserve urban “green spaces” in cities like Toronto proliferate, it is vital to reexamine the frameworks employed to communicate these issues to the public. A sub-section of recent biodiversity conservation analysis has examined the rhetorics that global neoliberal systems have employed to undermine traditional ways of regulating the natural environment (notably: Brockington and Duffy 2010; Macdonald 2010; Brockington and Igoe, 2010). Contributing to this literature, this paper critically examines the rhetorical maneuvers at work in Toronto’s Draft Biodiversity Strategy, focusing on the ways that “harm” is constructed and how these frameworks are put to work. In particular, this paper uses invasive species as an example of a “harm” framework that diverts public attention from the de-regulation of natural spaces that the conservation movement arose to combat. The case studies for this paper begin to examine this tension in three current cartographical frameworks in Ontario and the policies that shape and make use of these frameworks. Through these case studies, this paper begins to elucidate the written and visual rhetorics that Toronto’s DBS must critically analyze before developing their maps. To resist neoliberal ideologies that deregulate natural spaces, this paper makes the case for developing public communication frameworks that are intensive, adaptable, and locally informed. Explicitly engaging with the rhetorics that legitimize these extensive systems locally allows public communicators to resist (if only temporarily) the re-deployment of these local frameworks for global neoliberal aims.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 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 teacher head, 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".