Bibliographic record
Abstract
It’s so good to be in Cambridge, it feels almost like home. Let me start by stating that in the past year ITMB, a successful map publisher in British Columbia, Canada published more paper map titles than at any time in their history. Similarly, the U.S. Geological Survey (USGS) recently announced that they have ceased producing paper from their aerial photography archive and will only produce digital copies. I believe that both of these facts speak to the future of maps and digital data. It means there will be paper maps well into the future and there will be an increasing array of digital data - some of it reformatted, as in the USGS case, and most of it will be born digital. When asked to speak about GIS and its role in libraries I often find myself in a conundrum - am I here to slay the dragon, or to pet the dragon. The role of technology in libraries is not one that has been embraced by everyone, and often the technology itself seems to have been force-fed upon us. The library profession is not one that has historically been a proponent of change and the very nature of GIS is change. In one sense, we have been given the choice of becoming paper museums or, at the very least, making GIS technology available in our collections. Today, I would like to review the many ways that GIS is, or will, affect our collections. I will divide the presentation into a general overview of GIS in libraries, how it affects our acquisitions or collection development policies, its effect on cataloging, on reference services, staffing, and our web services. Then I will shift the focus a little and discuss the current situation at the Harvard Map Collection, the future role of legacy collections, and a look to the future.
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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".