A Future for Public Service Television
Bibliographic record
Abstract
A guide to the nature, purpose, and place of public service television within a multi-platform, multichannel ecology. Television is on the verge of both decline and rebirth. Vast technological change has brought about financial uncertainty as well as new creative possibilities for producers, distributors, and viewers. This book examines not only the unexpected resilience of TV as a cultural pastime and aesthetic practice but also the prospects for public service television in a digital, multichannel ecology. The proliferation of platforms from Amazon and Netflix to YouTube and the vlogosphere means intense competition for audiences traditionally dominated by legacy broadcasters. Public service broadcasters — whether the BBC, the German ARD, or the Canadian Broadcasting Corporation — are particularly vulnerable to this volatility. Born in the more stable political and cultural conditions of the twentieth century, they face a range of pressures on their revenue, their remits, and indeed their very futures. This book reflects on the issues raised in Lord Puttnam's 2016 Public Service TV Inquiry Report. With resonance for students, professionals, and consumers with a stake in British media, it serves both as a historical record and as a look at the future of television in an on-demand age.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.017 |
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".