Bibliographic record
Abstract
In this paper we report on the experience of two research projects that intended to experiment with crowdsourcing models for opening up their scholarly materials to the wider public. Both the Howitt & Fison project, and Mapping Print; Charting Enlightenment were designed to take into consideration particularities of the Australian academic environment: in the former case, sensitivities around materials relating to First Peoples; in both cases, geographical distance from potentially interested communities, and the difficulties of formal recognition and categorisation of time spent on activities that lie at the intersection of research and outreach. They had similar challenges in terms of needing to process a large amount of data before analysis and progress towards the projects’ main research goals could begin. They also had similar goals in terms of eventual use of the project data, for example, making historical texts available online, and producing maps, networks, timelines and digital exhibitions of images and texts. In the end, one project has found crowdsourcing invaluable for building connections with interested publics the other discovered that crowdsourcing was not necessary to produce the results the project needed, and has moved away from this to focus its efforts instead on the linking of existing data and automation of structuring and categorisation. This paper discusses how the projects came to take these different directions, and how the above-mentioned Australian contexts contributed to their evolution.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".