Public Service Media and Diversity in the Digital Media Landscape: Opportunities and Limitations for Social Justice
Bibliographic record
Abstract
This essay reviews the place and role of Public Service Media (PSM) in promoting social justice in the changing digital media landscape through the ethos of diversity. Media diversity – the value and practice of including varied viewpoints, social groups, voices, and channels or outlets in media – has long been a declared pillar of PSM organizations worldwide. However, current changes in the digital media landscape and the growing extension of PSM organizations to digital platforms require re-reading the premise of promoting media diversity as a tool for social justice. This essay identifies a paradox. On one hand, online media appear to accommodate a greater range of diverse voices and players, particularly in the PSM ecosystem. At the same time, these very same online spaces jeopardize diversity, as the increasing practices of personalization, algorithmic curation, and platformization often reduce diversity of representations, voices, and exposure to content, thereby hindering opportunities for social justice and equality. This essay shines a spotlight on this nexus of conflicting mechanisms. The essay begins with in-depth definitions of the two somewhat convoluted terms diversity and social justice. This section includes a review of global perspectives on PSM organizations, and the long-standing value of diversity promoted through them for decades. We then review the impact and changes identified in PSM platforms worldwide in light of the digital turn in the media field. This is followed by an in-depth discussion of the inherent tension between mechanisms that promote and hinder diversity online. Throughout this discussion, we raise questions about the practical dimensions of using online media to promote social justice through diversity. It provides a useful starting point for considering the intersection of Public Service Media, online platforms, diversity, and social justice. Thus, the essay will serve academic scholars studying social justice on a conceptual level as well as practitioners and stakeholders in the media industry at large, and PSM in particular, seeking to practically promote social justice.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.034 | 0.050 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.008 | 0.009 |
| 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".