Knowledge, Access, and Resistance: A Conversation on Librarians and Archivists to Palestine
Bibliographic record
Abstract
In June 2013, 16 librarians and archivists traveled to Palestine to connect with Palestinian librarians, archivists, activists, and organizers, to learn more about their struggles and their work. We came from the U.S., Canada, Sweden, Trinidad and Tobago, and U.K./Palestine. We were public, academic, and school librarians; movement archivists; artists; and activists. We spent eight days traveling throughout the West Bank and ’48. In the few days that followed, participants went off on their own to participate in follow-up meetings and to connect with people we hadn’t had time to meet during the first week. We reconvened at the end for a public event in Ramallah. Most of us did not know each other beforehand. Hannah has led many delegations in Palestine over the past decade, but this was the first specifically with librarians and archivists. Vani traveled to Palestine with an LGBTQ delegation in January 2012. Both live in New York and work with Adalah-NY: The New York Campaign for the Boycott of Israel. For this article, we sat down in August 2013 to reflect on the Librarians and Archivists to Palestine delegation. We recorded and transcribed our conversation, and edited and rearranged it slightly for the sake of clarity. It should be noted that we often refer to the U.S. in our conversation, because this is the context within which the two of us work. Not everyone from the delegation lives in the U.S.
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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 | 0.041 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.102 | 0.053 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".