Shared Spaces: Funding and Managing Libraries and Parks in Tough Times
Bibliographic record
Abstract
Public parks and library systems across North America are experiencing fiscal distress. Municipal budgets are increasingly strained to cover the costs of new facilities and the maintenance and operating costs of existing facilities. In a slow-growing economy, assets such as parks and libraries compete with policing and municipal labour for funding in the municipal budgeting process. The Institute on Municipal Finance and Governance (IMFG), with the support of TD Bank Group, held a series of three lectures in spring 2012 to raise awareness of the importance of shared public spaces such as libraries and parks to the health of our city. At a time of fiscal restraint, these shared spaces are often vulnerable to funding cuts. Short-term cost-cutting measures galvanize the community to oppose the reductions, but are there longer-term solutions to the funding problems? Are there new funding models to meet these challenges, and where are they being implemented? Speakers from Chicago, Philadelphia, Los Angeles, and New York addressed these questions.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".