Parametrizing ‘the digital’: education research methods for platform ecologies
Bibliographic record
Abstract
This conceptual article provides an outline of Manuel DeLanda’s concept of ‘parametrization' and its methodological possibilities for inquiry into emerging platform ecologies in education. Traditionally, education research has treated ‘the digital' as separate from the analog. However, transdisciplinary literature has shown how connective technologies blur these distinctions, expanding the scope of education research to include the interplay of social, technical, and political-economic relations within ‘the digital.' This complexity presents challenges for researchers in prioritizing aspects of these relations. To address this tension, we turn to DeLanda’s ‘parametrization' for setting inquiry parameters with ‘control knobs' to adjust the focus on relevant actors, activities, and interactions. By examining the influence of digital platforms like Google in educational settings, we illustrate how parametrization allows researchers to navigate scales and relations, offering insights into the nuanced impacts of digital technologies on teaching and learning practices.
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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 | 0.118 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".