Bibliographic record
Abstract
When Lyndon Ormond-Parker, Aaron Corn and Cressida Fforde envisaged convening the first-ever Australian symposium on Indigenous use of information technology and communications, they imagined a small gathering of people like themselvesscholars and practitioners of Indigenous cultural heritage management and expression enabled by digital technology to communicate locally and globally.When they received responses to their invitations, the numbers were overwhelming and they came from across Australia, from people working in fields as diverse as art, media, teaching, language, mapping, archival research, dance and performance, ethnomusicology and museums (see AIATSIS 2009; also AIATSIS 2010a also AIATSIS , 2010b)).The contributors to this book, Information Technology and Indigenous Communities, are working at the cutting edge of their cultural, geographic and disciplinary fields.The sheer innovation, as well as the global reach of Australia's Indigenous communities working with these new technologies, becomes clear in each of these chapters.That information technology and communication is now a major industry in Indigenous communities across Australia is evident and demands a comprehensive response from governments and service providers.In Paris in 2001 I said at the international symposium 'Indigenous Identities, Cultural Diversity and Indigenous Peoples: Oral, written expressions and new technologies':The Internet differs from other media in that individuals have a high degree of control over the material they can access.Carriage and content are separate, and those who manage the wires are not the same as those who decide what goes down them.This democratic aspect of the Internet encourages the development of networks of individuals who have particular interests.Of course, in community [knowledge centres], access to computers is low, reflecting the proportionally high costs for individuals on relatively low incomes.However, once a threshold has been reached, through schools and community libraries having computer access, Information technology and Indigenous communities vi
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".