New Media and the Public Sphere: Creating Better Public Value in the Emerging Digital Economy
Bibliographic record
Abstract
The Canadian Broadcasting Corporation (CBC), like many other public service media outlets across the globe is facing a challenging time in its history. The multi-channel environment has become even more complex with the introduction of new media broadcasting undertakings that are further fragmenting a converging marketplace. The CBC is a unique example of public service broadcasting that highlights the growing complexities of new media technologies. With its deeply-rooted history as an instrument for the cohesion of national culture, it is part of a larger, more complicated system of Canadian broadcasting: a hybridized model facing significant strain due to rapidly-changing technology and funding cutbacks. Unlike other Canadian broadcasters, the CBC is facing a precarious situation with an affront on both sides: decreased funding from the federal government and decreasing share in an increasingly fragmented media market. Given its extremely broad and wide-reaching mandate, it is not surprising that the CBC is under considerable duress. In this paper, I will explore the utilization of new media technologies as a potential avenue for monetizing the public broadcaster amidst these challenges as we attempt to answer the question of money for public value in public service broadcasting (PSB), arguing that value holds both economic and political considerations that we
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".