History and IS – Broadening Our View and Understanding: Actor–Network Theory as a Methodology
Bibliographic record
Abstract
The call for historical research In IS, mirrored In other fields of business studies, Is an explicit recognition of the predominance of presentism in business research; the use of the past only to justify and validate current beliefs or inserting modern beliefs onto the past, rather than using the past to understand and reveal current assumptions and biases. There is freedom in severing time and centering ourselves and our artifacts (computer technology), looking to improve the future unburdened by the past. Yet, if that assumption is wrong, and the present is instead fluid and unstable because the past embedded in the present is tension filled and unresolved, this raises fundamental challenges to the work that we do, the value of that work to others, and is cause for reflection on our impact as educators. This paper demonstrates the merits of using Actor-Network Theory as a methodology for historical IS research, through its use in a Canadian case study. The study was prompted by the apparent resolution of a privacy controversy, involving personal motor vehicle registration information in the province of Alberta, through an appeal to something called ‘historical purposes and practices.’ Strangely, the purposes and practices were never identified. This begged the question, ‘what was the substance of this argument and how come it was successful?’ Tracing actual ‘purposes and practices,’ from the early 1900s to the present, reveals how historical, contextual understanding offers not only insights into, but can alter our very understanding of, the present.
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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 | 0.035 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.070 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".