Bibliographic record
Abstract
While networks have proliferated in literature and in our health system, our day-to-day language has not kept up in sophistication. This commentary builds on the work presented by Huerta, Casebeer and VanderPlaat to further explore the language of networks. An expansion of our "network literacy" needs to be reflected in a broader vocabulary for describing particular networks and identifying patterns of relationship that are not appropriately labelled a network. Dimensions along which network managers often understand and place their networks are reported, and the implications of various network images are considered. The distinction between the image of a fishing net and that of a spider's web explores the difference between networks as system substrates and as centres. A moratorium on the term network is called for, to ensure an expanded vocabulary is applied to emerging new relationship patterns between or independent of organizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.020 | 0.051 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.053 | 0.067 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".