Bibliographic record
Abstract
The essays in Small Tech investigate the cultural impact of digital tools and provide fresh perspectives on mobile technologies such as iPods, digital cameras, and PDAs and software functions like cut, copy, and paste and WYSIWYG. Together they advance new thinking about digital environments. Contributors: Wendy Warren Austin, Edinboro U; Jim Bizzocchi, Simon Fraser U; Collin Gifford Brooke, Syracuse U; Paul Cesarini, Bowling Green State U; Veronique Chance, U of London; Johanna Drucker, U of Virginia; Jenny Edbauer, Penn State U; Robert A. Emmons Jr., Rutgers U; Johndan Johnson-Eilola, Clarkson U; Richard Kahn, UCLA; Douglas Kellner, UCLA; Karla Saari Kitalong, U of Central Florida; Steve Mann, U of Toronto; Lev Manovich, U of California, San Diego; Adrian Miles, RMIT U; Jason Nolan, Ryerson U; Julian Oliver; Mark Paterson, U of the West of England, Bristol; Isabel Pedersen, Ryerson U; Michael Pennell, U of Rhode Island; Joanna Castner Post, U of Central Arkansas; Teri Rueb, Rhode Island School of Design; James J. Sosnoski; Lance State, Fordham U; Jason Swarts, North Carolina State U; Barry Wellman, U of Toronto; Sean D. Williams, Clemson U; Jeremy Yuille, RMIT U. Byron Hawk is assistant professor of English at George Mason University. David M. Rieder is assistant professor of English at North Carolina State University. Ollie Oviedo is associate professor of English at Eastern New Mexico University.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".