Forging the Future of Special Collections. Arnold Hirshon, Robert H. Jackson, and Melissa A. Hubbard, eds. Chicago: Neal-Schuman, 2016. 202p. Paper, $85.00 (ISBN 978-08389-1386-4).
Bibliographic record
Abstract
In October 2014, more than two hundred archivists, book collectors, donors, and librarians from the United States and Canada convened at Case Western University in Cleveland, Ohio, to discuss the state of Special Collections in North American libraries. The conference, “Acknowledging the Past, Forging the Future,” was hailed as “a milestone event in assessing the past and projecting the future of special collections.” The edited collection Forging the Future of Special Collections is a product of the sessions and “expands and enriches the ideas presented at the colloquium by including significant additional material from the contributors” (xv). The featured volume of essays is composed of three sections with 17 total chapters. Each chapter summarizes the revised remarks of event commentators and includes an introduction by Robert H. Jackson. Jackson points to the importance of the collection of essays, noting, “The implication is that the future of the book is in our hands. We will control it. We will shape it. The decisions we make as readers, collectors, and special librarians today will determine what happens to our fields tomorrow. This is a hopeful message, and this book presents a hopeful future as well” (xv).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".