Information <em>in</em> the ecosystem: Against the “information ecosystem”
Bibliographic record
Abstract
The “information ecosystem” metaphor is widely used in academic libraries and has become nearly ubiquitous when speaking of the information systems that support scholarly communication and varied forms of data sharing and publication. The trending use of this language arises from non-academic applications — for example in big data (the Hadoop ecosystem) or software development (the node.js ecosystem) — and there remains little critical examination of the use of this metaphor. Indeed, the definition of ecosystem as the set of relations between living organisms and their surrounding non-living environment is apparently not directly a part of the metaphor. This paper first describes the emergence of ecological thinking and how it was influenced by early information science and then explores how different “ecologies” are used within the academy, including in the emergent field of information ecology. A short critique of the metaphor is then posed and the paper concludes that the information ecosystem metaphor is useful, yet at the same time there are dangerous elements that render aspects of human societies and natural ecosystems invisible.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".