Sustaining an Online Information Network for Non-Profit Organisations: The Case of Community Exchange
Bibliographic record
Abstract
Community Exchange (CE) is the most successful online information network for the non-profit sector in Ireland. CE is also an online counter-public sphere, a space where alternative messages can be produced and exchanged outside the mainstream media and public sphere. CE emerged within specific Irish research, policy and practice contexts. It consists of a free-to-users email bulletin and website containing news and information produced by and for the community and voluntary sector. Since its modest beginnings in October 1999, CE has grown to include more than 3,600 subscribers in the Irish Republic and Northern Ireland, primarily staff and volunteers from non-profit organisations. This paper contributes practical experiences from Ireland about an online community information network for the non-profit sector, including a discussion of its sustainability. The results of a recent survey of CE subscribers demonstrated strong support for CE and a resistance to introducing commercial advertising or paid subscriptions, highlighting the challenge to sustainability. The case study of CE suggests that an online counter-public sphere can be sustained in Ireland with the ongoing contribution of considerable volunteer resources. The study suggests that financial sustainability is the biggest single challenge for the sustainability of a counter-public sphere.
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.022 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".