Bibliographic record
Abstract
In this paper the author tells the story of how the technology she used in her research reshaped her thinking about her research as she reshaped the technology for purposes beyond its initial intent. Her research story provides a real-life example of the dialectical relationship between humans and technology. When she immersed herself in a multimedia authoring environment called Flash in the process of her research, the experience led to the reorganization or restructuring of her thinking, and her research looks very different than it would have if she had used technology only as initially planned. She discusses the performative potential of new media and, in particular, a digital environment she created to store, organize, and represent her data, and she discusses the role of the digital environment in providing a meeting place for the participants and the researcher in the study. Thinking with new media on an ongoing basis in her study meant thinking about research data, analysis, and presentation through the lens of new media's affordances: multimodality, multilinearity, and performance.
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 | 0.115 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.020 | 0.120 |
| Scholarly communication | 0.032 | 0.033 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.004 | 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".