Exploring the potential for crowdsourced spatial information to inform debate related to the changing Canadian local news landscape
Bibliographic record
Abstract
This paper investigates an applied participatory mapping research project that enables members of the public to crowdsource information for Canada’s first community spatial database documenting contemporary changes to local news outlets. This data is presented on the Local News Map, which locates where news organizations are closing or cutting back services and where new outlets are launched or services are increased. The tool, released in mid-2016 by Canadian academics, is a web-based, interactive platform that displays map markers and descriptive information about changes to local television, radio, online sites and newspapers dating from 2008 to the present. Map filters allow users to select and view specific information about these changes. We explore the impact of the project, demonstrating that the map has enriched the public sphere by generating robust empirical data on news outlets that have shut down or launched or increased/decreased service in their communities. Specifically, we found strong and ongoing demand from journalists who use the platform’s data to provide context for stories about the ongoing disruption in the local news sector. We also identify other impact indicators for further investigation.
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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 | 0.027 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".