AI Operationalisation Chasm: Evidence from Canadian Public Administration
Bibliographic record
Abstract
Artificial intelligence (AI) provides immense opportunities for an efficient and lean public administration. However, since AI is a general-purpose technology, there is a need for conducting AI fitness assessments against organisational-specific problem(s). This requires a pilot stage before an adoption decision is made. However, despite several promising AI pilot projects underway within the Canadian public administration, few have transitioned into production solutions. Through a qualitative study based on in-depth semi-structured interviews (n=37) within Canadian public administration, this paper explores the AI adoption process. The results enumerate two pathways to AI initiation: problem-driven and solution-problem pairing. The paper identifies the existence of a significant AI operationalisation chasm as a major barrier to operationalising AI pilots. This chasm results from technical debt, silos, and a lack of processes for managing AI tensions. The paper contributes to the AI adoption and diffusion literature and provides practitioner recommendations for crossing the AI chasm.
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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 | 0.044 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".