Bibliographic record
Abstract
:15 p.m., Oct. 10, 2001: I had just left work and was headed west on Canyon to pick up a Nick-N-Willy’s pizza for dinner. On the corner of Canyon and Broadway in front of the Boulder Municipal Building, an unexpected silence and stillness caught my eye. The silent stillness was dressed in black and female. Among the 80 to 100 assembled women were friends and colleagues. I recognized a familiar nose and a beloved head tilted to one side. I was stunned and delighted. Who were these women? What were they doing? What was their purpose? Their message? I rolled down my window to hear what they had to say, but this was a feast for eyes, not ears. No petitions to sign. No candidates to promote. No initiatives. No money to raise. Except for a lone banner that I couldn’t read, the customary signifiers of social action were strikingly absent. I was taken aback by the powerful collective presence: a steady, alert gaze and relaxed posture. Were these women practicing street-corner standing meditation? Drive-by communion? Performance art? The light changed from red to green, and reluctantly I accelerated to keep pace with the congregation of buses, trucks, and homeward-bound motorists. But something had shifted. Eighty local women publicly demonstrating silence bounced the post 9-11 commentary out of my brain. I was touched by an unwritten law of social and spiritual action: once you’ve been touched, you desire to touch. The next day I ran into friend and colleague Anne Parker whose silhouette I had seen in the twilight. “I saw you on the corner of Broadway and Canyon last night. What was that?” “Women in Black.” “What’s Women in Black?” Women in Black started in Israel in 1988 with a small group of women protesting the occupation of the West Bank and Gaza. Jewish and Palestinian women stood together at a busy intersection in Jerusalem once a week. Women in Black caught on, stretching across scores of war zones to Australia, Canada, Europe, and the U.S. In Boulder, two local women organized WIB by getting on the phone and calling friends. They decided if no one else showed up, they would simply stand together on the corner of Canyon and Broadway from 5-6 p.m. on Wednesday evenings. But others did, and continue to, show up. 5:15 p.m. Oct. 17, 2001: I’m standing on the corner of Broadway and Canyon dressed in black coat, pants, gloves, and boots. The driver of a US West truck leans out the window, grins, and waves. A woman with a crying baby in her arms hurries to the bus stop on Broadway. Two women in black toting backpacks walk across Broadway. The woman next to me steps to her left while I step to my right, making room for the newcomers, exercising the permeable boundary between “actors” and “audience.”
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.033 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".