Bibliographic record
Abstract
This article examines how certain neighbourhoods and the populations that reside in them might be understood as safe and good by presenting observations from a walking, autoethnography through and around the suburb in which the author resides. The messages that societies receive and internalize about people who experience poverty are primarily constructed out of neoliberal institutions that uphold the idea that those who live in poverty are there by choice or incapacity and face the appropriate consequences of that choice. Neoliberal discourses devalue the lives of those experiencing poverty by suggesting that they are morally, physically, or mentally incapable of being responsible for themselves. While anyone could potentially experience poverty, the relational construct of the upper class/lower class creates a metaphorical divide that requires deep rethinking to transcend. When spaces are demarcated as unsafe or violent, other spaces are relationally marked as safe or secure. The article concludes that controlling outward appearances largely creates and reinforces constructions of suburban areas as safe in relation to the construction of other areas as unsafe and violent. However, the intensive focus on controlling appearances leads to a mistrust of others and the sacrificing of communities that once existed and thrived.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.063 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".