The intersection of social networks in a public service model: A case study.
Bibliographic record
Abstract
Examining human interaction networks contributes to an understanding of factors that improve and constrain collaboration. This study examined multiple network levels of information exchanges within a public service model designed to strengthen community partnerships by connecting city services to the neighborhoods. The research setting was the Neighbourhood Integrated Service Teams (NIST) program in Vancouver, B.C., Canada. A literature review related information dimensions to the municipal structure, including social network theory, social network analysis, social capital, transactive memory theory, public goods theory, and the information environment of the public administration setting. The research method involved multiple instruments and included surveys of two bounded populations. First, the membership of the NIST program received a survey asking for identification of up to 20 people they contact for NIST-related work. Second, a network component of the NIST program, 23 community centre coordinators in the Parks and Recreation Department, completed a survey designed to identify their information exchanges relating to regular work responsibilities and the infusion of NIST issues. Additionally, 25 semi-structured interviews with the coordinators and other program members, collection of organization documents, field observation, and feedback sessions provided valuable insight into the complexity of the model. This research contributes to the application of social network theory and analysis in information environments and provides insight for public administrators into the operation of the model and reasons for the program's network effectiveness.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".