Bibliographic record
Abstract
Purpose – The purpose of this paper is to bring both Simondonian and Deleuzian insights to bear upon the nature of documents and documentation by viewing them as non-representationalist, and as products of transduction and reticulation that render documents assemblages that are in constant negotiation with an environment as instances of a perpetually renewing problematic. Design/methodology/approach – Simondon's work on metastability and transduction can offer particular insights into how the author views documents in terms of their materiality, signification, and possibly to move beyond the phenomenological bias in the treatment of documents. Findings – In understanding or describing the process of documentation as a reticulation or unfolding, the author also comes to view the document as an assemblage in perpetual negotiation. This paper adapts Deleuze and Guattari's articulation framework of expression-signification and provides a bit of groundwork towards two registers of information (first and second order) according to the preindividual process of that allows for the individuation of documents. Originality/value – This paper makes an original contribution to understanding the process of documentation and the product of documents in a more fluid, interdynamic context by shifting or displacing the traditional view of information.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".