Seeing the Forest for the Trees on Mars: Locating the Ideology of the “Library of the Future”
Bibliographic record
Abstract
For many decades now library practitioners have been generating a vast literature concerned with the “library of the future.” While much of this literature may be classified according to its imperatives for radical versus incremental change, what is largely absent from these articles is a theoretical understanding of the underlying ideological bases of their arguments, as well as extrinsic or transdisciplinary perspectives. Reconsidering these prescriptions for the future of the library through the lens of futures studies has the potential to afford us critical perspectives on their ideological foundations. Hal Niedzviecki’s 2015 book Trees on Mars: Our Obsession with the Future is analyzed to locate the ideological tensions in LIS literature between chasing the future on the one hand and cherishing the security of tradition on the other.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.025 | 0.066 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".