Holistic information research: From rhetoric to paradigm
Bibliographic record
Abstract
Abstract Many researchers in library and information science have claimed that studies that are holistic are critical to understanding various phenomena. On closer examination, however, the term “holistic” is used mainly as a rhetorical device in the literature, rather than as one that embraces the epistemological tenets of a holistic paradigm, and applies these to research design. This paper examines this rhetorical use, and explores what it would mean, and why it would matter, to adopt substantively holistic approaches to research. We review relevant literature in library and information science to position past uses of holistic and compare these to the conceptual intentions of holism . We also outline the concept of holism , itself, with a focus on methodological and ontological holism , which can most deeply inform research design in our discipline. Greater methodological diversity, including much wider adoption of interpretivist and critical approaches, can address the concerns underlying the use of holistic rhetoric. We illustrate this central conceptual argument with a roadmap illustrating holistic considerations throughout the research process. The paper demonstrates that it is possible to shift away from predominantly rhetorical use of holistic , toward paradigmatically holistic research, which will provide for richer analyses of critical phenomena in the discipline.
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 | 0.060 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.092 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".