Bibliographic record
Abstract
In 2010, Keith Aoki and I coined the phrase 'immigration regionalism' to describe a proposed innovation in immigration law and policy reform. Our intention was that immigration regionalism would become Immigration Regionalism — a book-length articulation, argument, and analysis of the provocative idea — in hopes that others would take up, critique, expand, revise, and operationalize this notion, in other words: help to answer our query as to whether 'immigration regionalism is an idea whose time has come.' Thus, without Immigration Regionalism, and without Keith, immigration regionalism necessarily remains incomplete. Given Keith’s love of music, his talent and background as a musician, his distinctive collaborative style of riffing-and-jamming, and his prolific career forged by crossing genres and media, I regard the status of our work on immigration regionalism like the first song of an unfinished album: Immigration Regionalism. Perhaps just as important as what we discussed is what we did not discuss before Keith passed away on April 26, 2011. Specifically, we had not written about these basic topics: what is a region; how and why are regions defined and who defines them; what is regionalism; what is the connection between regions and regionalism; what meaning or influence might regionalism have in the context of immigration law and policy; and what might count as an immigration region. I want to begin to address those topics here. In paying my respects to the influence of Keith’s work and thought, it feels right to continue with the focus of our collaboration and to reflect upon and share with others the distinctiveness of how Keith worked. How Keith thought through and worked out ideas with others was utterly refreshing, both professionally and personally speaking, and it is part of what so many of us dearly miss. With this Article, I mean to help bring our unfinished album nearer to completion. I do so here both by sharing the genesis and formation of immigration regionalism and by discussing and employing the methods by which we worked. I use a song structure framework as the organizational framework for this piece, both in homage to Keith and in keeping with our style of collaboration, and I utilize eco — the recalling of previously played notes, though softly and in a different octave — as I work to advance this half-written song toward a coda (repeat) and fade. My hope is that Keith’s voice, as well as his thought, vision, and inspiration, remains resonant here and in any future work on immigration regionalism.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".