Bibliographic record
Abstract
In this chapter (see Table 4.1), we provide a brief history of virtual assistants, emerging as technocultural entities eventually to serve a role in writing and work practices. We illustrate how automation changes writing collaboration between humans and nonhuman agents, leading to “superteams” (Deloitte) and the seamless integration of AI and other automated processes into workplace teams (Seeber et al., in Information & Management 57: 2020). As AI writing advances to include further communication practices through Natural Language Generation (NLG), we discuss the concept of cowriting with AI systems. We explore how literacy practices must adapt to include AI literacy, discussing the role of professional and technical communication (PTC) educators and professionals in meeting these goals. We emphasize the issue of changing roles and risks which arise as innovation advances before appropriate ethical and regulatory regimes are put in place and how AI bias has led to human rights violations. We provide case studies of how emerging technologies are inculcated in dataspheres that have inappropriately excluded racialized people through the rise of black boxes and exclusionary algorithms. AI Explainability and Transparency have been flagged as necessary principles for developing this technology, with sectors calling for protocols to explain highly complex tech for human actors.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".