SKILLS AND LEARNING FOR CREATING SUSTAINABLE COMMUNITIES IN ONTARIO, CANADA
Bibliographic record
Abstract
The paper presents a pilot study based on a survey of skills and learning in sustainable community development in Ontario, Canada for a country-wide research on place-making for building sustainable communities. Place-making is a transformative process of planning, designing and managing places, with people in mind. By definition, a 'small-urban municipality' (SUM) is a city or urban area with a population of 60,000 that have adopted an environmental action plan and/or economic development strategies to achieve economic prosperity and community sustainability. The research examines the 'skills question' in the labour market such as job mismatch, skills squeeze and shortage of critical talent in building sustainable communities. Its overarching goal is to provide insights on the learning needs, skill patterns and future capacities for sustainable community development (SCD) to establish highly skilled professionals in building sustainable communities. Its focus is on communities representing Southern and Northern communities that meet the population criterion. The pilot survey's target population is 300 professionals of three groups. Group 1 is from local government (Mayors and/or Reeves) to get a local policy perspective; Group 2 from core occupations and professions comprising a broad mix of built-environment professions and public service professionals; and Group 3 are related professions such as regeneration officers and social workers. Group 2 professionals range from landscape architects, urban designers, engineers, environmental officers/managers, housing and welfare officers, urban planners, energy planners and economic development officers/managers. A survey of generic, specialist and technical skills and knowledge of future professionals were made for acquired and required skills by profession and group. For data analysis, the Likert scale data are to be analyzed using Mann-Whitney U (Zar 1996). Cronbach's Alpha is used to provide an internal consistency estimate of test score reliability (Cronbach 1951). R (R Development Core Team 2008) and SPSS 20.0 (IBM 2011) statistical software packages will be used for all analyses. Projections of skill and workforce scale gaps with an evaluation model on knowledge and skills on the learning capacities of SUMs will be done as the current data is still preliminary. In driving the skills agenda to establish the capacity requirements in SUMs, the rationale is to promote meaningful skills development and strategic learning strategy in sustainability education through programs and courses that are responsive, proactive and complementary to the demands of contemporary SCD practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".