DHSI 2022 Conference & Colloquium Special Issue of IDEAH: Introduction
Bibliographic record
Abstract
chaired by Caroline Winter (U Victoria).It was one of seven aligned conferences or events.As in the previous two years, the 2022 iteration of the Conference & Colloquium was held virtually due to the COVID-19 pandemic.To facilitate participation across time zones, it featured pre-recorded conference presentations, digital posters showcased in an online exhibit, and live discussion of these materials.Additional conversations took place on twitter.comvia #DHSIConf and the general event hashtag #DHSI22.This special issue, which brings together a selection of papers arising out of the DHSI 2022-Online EditionConference & Colloquium, is the sixth such collection.In our introduction to the previous special issue, Lindsey Seatter, Caroline Winter, and I echoed many others before us by pointing out how, despite the pandemic, technology allowed members of our scholarly community to stay connected.Indeed, one of the unifying themes we identified for that special issue was "the role of infrastructure in shaping community" (Jensen, Seatter, and Winter).This year, we again observed the many ways that digital platforms and tools continued both to facilitate and to constrain connections between members of our dispersed networks.However, we were also struck by an apposite idea advanced during at least two of DHSI's Institute Lectures: digital infrastructure does connect people, but it may also be true that "infrastructure is people," as Leslie Chan remarked during his talk, "Is Open Scholarship Possible without Open Infrastructure?" (Chan; emphasis added).Chan's statement might conjure up images of precarious human pyramids or other acrobatic formations; more helpfully, and perhaps more to his point, we would argue that it serves as a helpful and necessary reminder of the human ingenuity, expertise, labour, and relationships that go into the creation and maintenance of digital infrastructure of any kind-of the tendency of infrastructure to be reliant on human factors.Chan illustrated this point with an image that, he noted, would likely resonate with many of us; indeed, some of us may identify with the lone researcher whose legacy project supports shaky digital infrastructure (Figure 1).For better or worse, then, we would echo Chan in asserting that if "infrastructure is people," it is also true that "infrastructure is relational."The question, perhaps, is how one can foster and maintain the human relationships that comprise digital infrastructure, ideally ensuring the long-term health and positive evolution of each.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.277 | 0.149 |
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".