Bibliographic record
Abstract
The belief that AI technology is on the cusp of causing a generalized social crisis became a popular one in 2023. While there was no doubt an element of hype and exaggeration to some of these accounts, they do reflect the fact that there are troubling ramifications to this technology stack. This conjunction of shared concerns about social, political, and personal futures presaged by current developments in AI presents the academic discipline of computing with a renewed opportunity for self-examination and reconfiguration. This position article endeavors to do so in four sections. The first section explores what is at stake for computing in the narrative of an AI crisis. The second section articulates possible educational responses to this crisis and advocates for a broader analytic focus on power relations. The third section presents a novel characterization of academic computing’s field of practice, one which includes not only the discipline’s usual instrumental forms of practice but reflexive practice as well. This reflexive dimension integrates both the critical and public functions of the discipline as equal intellectual partners and a necessary component of any contemporary academic field. The final section will advocate for a conceptual archetype—the Public Computer Intellectual and its less conspicuous but still essential cousin, the Almost-Public Computer Intellectual—as a way of practically imagining the expanded possibilities of academic practice in our discipline, one that provides both self-critique and an outward-facing orientation toward the public good. It will argue that the computer education research community can play a vital role in this regard. Recommendations for pedagogical change within computing to develop more reflexive capabilities are also provided.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.071 |
| Scholarly communication | 0.040 | 0.044 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 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".