Bibliographic record
Abstract
Representational logic cannot account for the entanglements of all that matters in making new media: feeling bodies, vibrant matter, feeling bodies and vibrant matter all moving and at different rates. In the currently shifting communicative landscape, where mobile technologies are the primary means for youths’ digital production, all this movement, all this moving matter, is integral to generating fuller, more (than) human expressions of youths’ new media making. This article therefore develops a non-representational theory of new media making through an intra-action analysis of five adolescents making a digital book trailer while moving within and across three locations. As guiding poststructural methodology, intra-action analysis attuned the authors to moments when bodies-materials-place became perceptibly entangled in the drawing of boundaries and exclusions. Analysis expresses how emergent (re)shapings of boundaries and exclusions across production settings were concurrent with a process of privileging text-based/media-based ideas and thereby various students’ becoming agencies and capacities to act as new media makers. The article concludes arguing that poststructural attention to literacy in the making matters as an ethical imperative for researchers and educators. Literacy in the making enacts boundaries and exclusions that participate in ongoing discursive-material practices, which have potential to produce histories differently in as yet unimagined futures.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.081 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".