Bibliographic record
Abstract
Wu, Frances - Audio Oral History Interview - CSWA ❧ Interviewed by Frances Feldman on June 2, 1999. An interview with Frances Wu as she discusses her early childhood in China; education; escape to Taiwan after Communist takeover of mainland China; study at McGill University; social work in New York with disturbed delinquent children; work at home for disturbed children in Michigan; reasoning behind job changes; problems of older Chinese refugees in United States; reasons for move to LA; doctoral studies at USC School of Social Work; organizing of Chinese-American Golden Age Association; loan applications for building for Chinese-American Golden Age Association; racial makeup of home for elderly of Chinese-American Golden Age Association; size of staff; board of directors; development of condominiums; Golden Age Village; prices of condominiums; HUD restrictions on finances; salary and personal finances; donations to education; activities for Golden Age Village residents; age of residents; Home Owner Association; acknowledgement of debt to USC School of Social Work. ❧ Frances Wu. Dr. Social worker. Director of retirement home. Interviewed by Frances Lomas Feldman. Date of interview: 6-2-99. 1 cassette tape. Length of interview: 1 hour and 31 minutes. Transcript of interview: 35 pp. CD containing interview and transcript. ❧ ADDITIONAL MATERIALS: 1. 1 diskette of transcript of interview. 2. Release re Dean's award for Outstanding Community Service to Dr. Frances Wu, from USC. 3. Resume. 4. Photocopy of title page of dissertation of Frances Wu. 5. Invitation to dedication of Dr. Frances Wu Chair in Social Welfare Policy and Services to Chinese Elderly.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.414 | 0.222 |
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".