Bibliographic record
Abstract
Dear Journal of Humanistic Psychology (JHP) Readers, As we reflect on the first quarter of the 21st century, the world needs the human in humanistic psychology more than ever.This is a precarious moment in human history.Devastating wars rage in the Ukraine, Gaza, Myanmar, the Maghreb, and Sudan.Climate change is transforming our planet and exponentially increasing numbers of climate refugees.Artificial intelligence is eclipsing taken-for-granted distinctions between human and machine.Existential psychologists often focus on the meanings of one fundamental aspect of our human being(s) that distinguishes us from machines: our mortality.In 2024, we entered the new year in mourning, having lost several luminaries in our field: Tom Greening, Donna Rockwell, and Miraj Desai.This year, we are mourning the losses of Eleanor Criswell and Paul Wong.We enter this new year with these visionaries in our hearts, and JHP is dedicated to furthering the work they were passionate about and continuing their legacies.We also enter 2025 in uncertainty.As I write this editorial, we are on the eve of an election that could radically change the sociopolitical fabric of the United States.Regardless of which candidate wins, we are in need of humanistic approaches to healing divisions and restoring our being-with, our fundamental togetherness with others in the world.We have a number of exciting plans for JHP in the new year.A new special feature called "Contemporary Humanistic Beacons," envisioned by past JHP Editor Shawn Rubin, will highlight specific humanistic programs,
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 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".