Free access to computers and the internet at public libraries: International reflections on outcomes and methods
Bibliographic record
Abstract
Abstract The Internet and computer technology have radically changed the way people live around the world. Public libraries have been at the forefront of championing digital inclusion through partnerships with the Bill & Melinda Gates Foundation, other international and national organizations, government, and their own communities. As a result, virtually every library in the United States, as well as many libraries in other countries, provides access (often free) to computers and the Internet. Similar to information and communication technologies (ICTs) and sometimes called public access computing (PAC), this access essentially encompasses access to digital resources, databases, networked and virtual services, training, technical assistance, and technology‐trained staff. Little research has been conducted, especially from a social policy perspective, on the broad impacts of these services on individuals, families, communities and nations. Discussion is also needed regarding how to study public access to computers and the Internet in libraries, highlighting the challenges of using mixed methods and team research. This technical panel comprises researchers from The Information & Society Center of the University of Washington Information School and Oslo University College, who are conducting several synergistic investigations of the impacts of access to computers and the Internet at libraries around the world. Upon introducing their respective studies (abstracts below), the panelists will engage the audience in an open discussion of the following questions. Note: The audience will “sign‐in” at the ASIST AM09 session and the ensuing discussion will be posted on the UW iSchool ISC website to document/promote future dialog with the global ICT‐PAC community. Session Discussion Questions What does “public access computing in libraries” mean in different geo contexts? What other terms are used for it? What impact does PAC have on individuals, families and society, over the short and long term? What difference does it make when libraries (as opposed to other venues) provide PAC? What are the challenges to studying PAC? What are the policy implications of PAC?
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 | 0.507 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.016 | 0.044 |
| Scholarly communication | 0.044 | 0.038 |
| Open science | 0.008 | 0.033 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 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".