A Social Media Strategy for Politics in Action: The Case of CPAC, the Cable Public Affairs Channel
Bibliographic record
Abstract
Social media is changing the way business is done, and television is no exception. This case study proposes a social media strategy for CPAC, the Cable Public Affairs Channel, as a means to transition from a one-way, television service to an all-encompassing source of political information and programming. CPAC is present in social media channels but they are under-resourced and underdeveloped. An element of trepidation exists amongst CPAC’s senior management with respect to social media, although there is an acknowledgement that CPAC must be in the space. Primary fears are that using social media will infringe upon the independent and editorial-free nature of its mission, as well as detract from intelligent and meaningful dialogue, making it a challenge for getting buy-in to do more. However, as broadcasters C-SPAN and PBS have demonstrated, social media can be leveraged in a way that does not threaten public interest media’s role but rather enhances it. Drawing on an extensive literature review, a focus group with CPAC’s senior management and interviews with comparator organizations C-SPAN and PBS, a strategy based on the findings is recommended for implementation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.019 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".