Practicing Media – Mediating Practice: Introduction
Bibliographic record
Abstract
More than a decade and a half after the concerted application of practice theory to media and communications, this IJoC Special Section “Practicing Media—Mediating Practice” aims to assess, apply, and expand on the diverse approaches to practice theory. Our focus on contemporary practice theories as well as empirical practice-based research comes at a time when the understanding of mediated and mediatized social life as practices is proliferating across research settings that may not explicitly engage with practice theory literature. This editorial introduction begins with the historical emergence of practice theories and the paradigmatic implications of engaging with practices as essential elements of the social world. Second, it considers the particular tensions emerging when considering both mediating practices and practicing media. In so doing, we outline the contributions to this Special Section, dividing them into three sets of articles that deal with (a) media practices as constituent elements of the social; (b) the employment of practice as a lens through which to make sense of processes of media production and its interpretation; and (c) the foundations of practice theories, their limits, and potential combination with other theoretical approaches.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".