The problem of agency; how humans act, how machines act
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A long-standing debate in the IS literature concerns the relationship between technology and organization. Is it technology that acts on organizations, or humans that determine how technology is used? Proposals for a middle way between the extremes of technological and social determinism have been put forward based on Giddens’ structuration theory, and, more recently, from actor network theory. The two theories, however, may be seen to adopt rather different, and potentially incompatible, views of agency (action). Thus, structuration theory sees agency as a uniquely human property, whereas the principle of general symmetry in actor network theory implies that machines may also be actors (agents). This rather fundamental disagreement may be characterized as the problem of agency. At the empirical level the problem of agency was played out in a Canadian telecoms company adopting an ERP system. Was it the mangers and unions (the human agents) that were determining the trajectory of the organization, or did the ERP system also play a role? This paper argues that neither structuration theory or actor network theory offers a particularly convincing account of the interplay of human and machine agency in this case. Since they cannot easily be combined, IS researchers need to develop more convincing theories which are focused on organization and IT. Some guidelines for this development are offered.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it