Local news poverty in Canadian communities: presentation to the House of Commons Heritage Committee
Bibliographic record
Abstract
This is an updated version of the brief submitted to the House of Commons Heritage Committee on Oct. 6, 2016 as part of the committee’s study on Communities and Local Media. Residents of Canada’s largest municipalities can obtain news from multiple sources, but it’s a different story elsewhere in the country. People who live in smaller cities and towns, suburban communities and rural areas have fewer options to begin with, and in recent years their choices have become even more limited. Traditional news outlets have been hit by cutbacks, consolidations and closures, while digital first news sites often struggle to stay afloat. Does any of this matter? The answer is “yes,” according to a report by the U.S.-based Knight Commission on the Information Needs of Communities in a Democracy. The commission’s report concluded that information is “as vital to the healthy functioning of communities as clean air, safe streets, good schools, and public health (Knight Commission, 2009, xiii).” It went on to argue that in addition to helping communities develop a sense of connectedness, access to information is essential in terms of holding public officials to account and making it possible for community members to work together to solve problems. While local journalism is the subject of increasingly intensive scrutiny by scholars in the United States – Duke University’s Philip Napoli, for instance, is launching a project that will investigate the state of local news in 100 U.S. communities (Napoli, 2016) - there is much we don’t know about the Canadian situation and the extent to which the critical information needs of rural areas, towns and smaller cities in particular are being addressed. As Carleton University professor Dwayne Winseck warned committee members during his testimony earlier this year, there are “severe” shortages of information on changes to the media landscape overall. Moreover, he cautioned, there are “a lot of opinions and little data to act upon” (Standing Committee on Canadian Heritage 2016, 5) .
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".