But I Need It! Exploring a Magic Quadrant for Collections Needs, Wants and Contributions
Bibliographic record
Abstract
Years and years of looking at Gartner Magic Quadrant charts has finally provided an inspiration for library adoption. The Gartner Magic Quadrant is a basic 2x2 matrix that serves as a decision support tool based on criteria along the X and Y axes. The Gartner model explores the "completeness of vision" with the "ability to execute" to rank the leading firms in any space into four major categories. In working with a vendor recently who showcased numerous faculty 'testimonials,' it dawned on me that we have a good use for a 2x2 matrix for library collections. We can use this to describe two factors, "the desire to have" and the "willingness to contribute." This is a core of the difficulty we have with sustainable budgeting for resources that might be critical, but there is no other funding available. It might be good as a way to explore priorities and encourage contributions from users for the resources we are being asked to fund.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".