Digital Technology Use and Future Expectations
Bibliographic record
Abstract
Problem, research strategy, and findings The implications of digital technologies for planning practice are receiving renewed interest in the wake of ever-improving capabilities in Big Data and artificial intelligence, as well as the rapid uptake of new technologies that allowed planners to work remotely during the COVID-19 pandemic. Despite this interest, there has been little cross-country comparative research regarding the adoption of technology within the planning profession and even less that addresses planners’ expectations and desires for future digital tools. We undertook a multinational online survey of planners in the United States, Canada, the United Kingdom, Australia, and New Zealand to gain a comprehensive understanding of current and expected future use of data and software in planning practice. Although the current use of data-intensive digital tools was limited, we found widespread expectations of change across the planning profession. Remarkable similarities were observed across the countries surveyed. The biggest differences in tech use were among planners undertaking strategic, specialist, and regulatory roles.Takeaway for practice Planning organizations around the world should prepare for a new wave of digital change as many technical obstacles that previously hindered the rapid exchange and analysis of vast amounts of data have now been overcome. Continued development of digital skills among planners is important but should be paired with career pathways for digital specialists within the profession. Planners should not complacently assume that adopting digital technologies will automatically lead to more effective and equitable planning outcomes. They should use digital processes to actively address biases in the underlying planning system.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".