Our Search for Intergenerational Rhythms as Tongan Global Scholars
Bibliographic record
Abstract
Our search for collective meaning-making across spaces and places as Tongan global scholars carries intergenerational rhythms. This article is a diasporic collaboration between members of the Tongan Global Scholars Network (TGSN), an online cultural collective drawn together through creatively critical rhythms and a desire to make space for ongoing criticalities through Tongan concepts, knowledge, and approaches. Employing the art of e-talanoa in our search for ways of crafting meaning, we unfold our narratives about TGSN’s humble beginnings using a range of modalities expressed as words, images, screenshots, and poetry. Our desire to connect early career scholars of Tongan heritage across the diaspora of Australia, the United States of America, Aotearoa New Zealand, and Tonga via the online space, led to enabling intergenerational relational rhythms between more seasoned and emerging scholars, sharing their understanding of Tongan knowledge and its relevance in the dominant Western academe. Intergenerational rhythms are central to TGSN’s survival. As a global network, TGSN continues to provide meaningful spaces for creatively critical meaning- making and intergenerational collaborative dialogue.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".