Social Dynamics of Expectations and Expertise: AI in Digital Humanitarian Innovation
Bibliographic record
Abstract
Public discourse typically blurs the boundary between what artificial intelligence (AI) actually achieves and what it could accomplish in the future. The sociology of expectations teaches us that such elisions play a performative role: they encourage heterogeneous actors to partake, at various levels, in innovation activities. This article explores how optimistic expectations for AI concretely motivate and mobilize actors, how much heterogeneity hides behind the seeming congruence of optimistic visions, and how the expected technological future is in fact difficult to enact as planned. Our main theoretical contribution is to examine the role of heterogeneous expertises in shaping the social dynamics of expectations, thereby connecting the sociology of expectations with the study of expertise and experience. In our case study of a humanitarian organization, we deploy this theoretical contribution to illustrate how heterogeneous specialists negotiate the realization of contending visions of “digital humanitarianism.”
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".