Bibliographic record
Abstract
As an education scholar and poet, I investigate experiences of fearfulness in order to promote learning how to live with a commitment to love. I have lived my life in fear, full of fears. Now I am practicing accepting myself, acknowledging the hollow in my heart, acknowledging the holes that sometimes look like gaping wounds but are really integral to my search for wholeness. And as I learn to know myself as a subject-in-process, a person under construction, I also learn to live in relationship with others in daily experiences written with words of patience, hope, and affection, for giving each other and all of us possibilities for living with love. Love is a commitment and a practice. Love involves a daily devotion. In order to love others, we must first love ourselves. We cannot learn to love by attending to the abstract and universal. We need to begin with small acts of love. We can begin with the person we are drinking coffee with. If we are going to change the world, if we are going to undo the damage and destruction of prejudice, hatred, and fear, we need to start with love. We need a curriculum of love. In order to learn the wisdom, philosophy, and practice of love, we need to acknowledge that we are not alone, independent, autonomous. We are part of a vast network of connections and interconnections, all ecologically sustained in rhythms and memories and hopes.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.035 | 0.039 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".