Media and Moral Education: A Philosophy of Critical Engagement (Laura D'Olimpio)
Bibliographic record
Abstract
Is a critical, compassionate spectatorship of mass media possible?This is the chief inquiry underlying Laura D'Olimpio's Media and Moral Education, which blends elements of ethics, art criticism, media literacy, and educational theory to make a case for the importance of philosophical thinking in enhancing technology-based communications.D'Olimpio argues in favour of what she calls "critical perspectivism": an ethical attitude toward the multiple perspectives presented in mass art and media, achieved through a balance of critical and compassionate engagement.She deems such an attitude especially important in a post-truth internet culture where information is abundant, rapidly changing, and rarely reliable at face value.In her view, a critical perspectivist is able to distinguish between the content and its consumer or creator, at least to the extent that they exhibit compassion for the latter as a fellow human being.They do this even as they lend a critical eye on the former through careful examination and argumentation.According to D'Olimpio's argument, if a critically perspectival attitude can enhance the process of deciding beliefs and values both on and offline, it becomes all the more crucial because such decision-making can in turn affect the moral treatment of others.In her opening chapters, she provides an overview of critical perspectivism's importance, then narrows in on its compassionate and critical features in her middle chapters, drawing on key ideas from Martha Nussbaum's neo-Aristotelian virtue ethics, as well as aesthetic accounts of mass art by theorists like T. W. Adorno and Gilles Deleuze.In her subsequent chapter on multiliteracies, through varied examples of recent multimedia trends, she describes what critical perspectivism might look like in practice, referencing online actions like #IllRideWithYou as prime instances of people challenging prevailing discriminatory stances in favour of more civic responses reflecting rational compassion (p.90). 1 Finally, in her closing chapter, in light of what she sees as critical perspectivism's moral potential in the face of technology's increasing grip on communications, D'Olimpio argues it should be integrated into young people's education, notably through pedagogical methodologies that can foster critical and compassionate engagement like the 1 This hashtag refers to the international viral response to severe racial profiling on social media during the Sydney Siege of 2014: while a terrorist gunman held people hostage in a café, thousands of Australians offered to meet local Muslim people to ride with them on public transport in solidarity against Islamophobic sentiment.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".