Working smarter and harder: an investigation of learning information technology in the Ontario Public Service sector
Bibliographic record
Abstract
This study investigates the learning and work relationships between information technology and the Ontario Public Service sector using a symbolic interactionist analysis and data derived from interviews with the workers themselves based on the 'Working ... IT' project (undertaken in partnership with CUPE-Ontario; project leader, Dr. P. Sawchuk). This piece details the stories of social benefits delivery workers who struggle daily to effectively service their clients using Service Delivery Model Technology (SDMT)--a software program implemented by the Ontario government in 2001. An examination of the formal and informal learning processes that workers employ in their daily routines reveals a whole new universe of language and meaning as they create what I refer to as a "culture of 'workarounds'" in order to bypass the inherent flaws in this software. Though the workarounds allow these workers to perform their duties more effectively, they are often troublesome and those who create them do so without financial compensation. Additionally, workers find themselves addressing the contradictions inherent in the attempts by management to change the nature of their occupation. Symbolic Interaction theory provides a useful posture on which to couch the differences between what is real and what is fabricated in the workplaces of these dedicated social service workers. Given a chance to speak after being silenced by an oppressive technological master, workers are quick to note the differences between the value of the formal learning provided by their employer and the informal learning that still takes place seven years after the introduction of this system. What becomes increasingly evident is that the workarounds they have created are not simply means by which they can achieve a greater end, they are in fact, survival tools they depend on to perform required duties.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".