Bibliographic record
Abstract
Humanities research with computing is frequently associated with three approaches to technologies: building infrastructure, designing tools, and developing techniques. The infrastructural approach is common among some libraries and labs, for example, where "infrastructure" implies not only equipment, platforms, and collections but also where and how they are housed and supported (Canada Foundation for Innovation 2008, 7). Tools, meanwhile, are usually designed and crafted with infrastructure. They turn "this" into "that": from input to output, data to visualization, source code to browser content (Fuller 2005, 85). Techniques are then partly automated by tools. Aspects of a given process performed manually may become a procedure run by machines (Hayles 2010; Chun 2014). Although these three approaches are important to humanities computing, today they face numerous challenges, which are likely all too familiar to readers of this handbook. Autoethnography, which is by no means new to the academy. Carolyn Ellis and Arthur P. Bochner provide a capacious but compelling definition of autoethnography, and we adopt it for the purposes of this chapter: "an autobiographical genre of writing and research that displays multiple layers of consciousness, connecting the personal to the cultural" (2000, 739). Our only edit is minor: "multiple layers of mediation and consciousness." For us, adding mediation to the mix of autoethnography is one way to engage computing (in particular) and technologies (in general) as relations. This means tools and infrastructures are more like negotiations than objects or products, and techniques are processes at once embodied (personal) and shared by groups and communities (cultural).
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".