The Local News Map : transparency, credibility, and critical cartography
Bibliographic record
Abstract
Widespread disruption has made tracking what is happening to local journalism in Canada a challenge. The Local News Map is a crowdsourced web-based mapping tool that invites the public to contribute information about local newsroom startups, closings, and service reductions/increases. As concerns mount about the future of local journalism, the map’s data are cited with increasing frequency and it has the potential to influence debate, policy, and other research. Taken at face value, the map is a straightforward tracking device. A critical assessment of the assumptions, decision-making, and biases underpinning the map, however, illustrates that nothing is ever quite that simple. Researchers have called for a more critical and reflective approach to the application of geographic information technologies to mapping. This article draws on theories of critical cartography to evaluate the Local News Map’s biases, limitations, and strengths with a view to enhancing its credibility as a research tool. Keywords : local news, mapping, local journalism, participatory mapping, crowdsourcing, critical cartography
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 | 0.054 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".