Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unifor recently held a one-day conference on Automation, New Technology and the Future of Work. As part of this event, the union released a discussion paper called The Future of Work is Ours: Confronting risks and seizing opportunities of technological change. With 315,000 members across the country in almost every sector of the economy, Unifor is Canada’s largest private-sector union. The union’s response to technological change is very much a work in progress, and the conference and discussion paper serve as a starting point in this process. The fundamental question at hand: how do we develop a worker-led program allowing us to get the best of technological change while avoiding the worst? We know that working people in Canada and around the world have been experiencing the negative impacts of technological change, and are feeling threatened and afraid for their own futures. At the same time, we have seen incredible opportunities for new and better jobs, union growth, and advancements in workplace health and safety due to technological change. Our responsibility as workers and as a union is to take control of this conversation and move from a position of fear and defence to a position of power and action. Making the transition means first understanding the problem, and a portion of our discussion paper and day-long conference focused on deepening our understanding of exactly what we’re talking about when we discuss technological change at work. Of course, our members know that technological change is nothing new. We have always experienced the effects of technological change, from the invention of the printing press, to the Industrial Revolution, to the invention of the modern computer. But what is new is the speed of that change. In just a generation, we’ve seen the invention of the internet and artificial intelligence, so-called ‘big data’ and the advent of mass surveillance, and the widespread use of advanced robotics and other automation. A flurry of negative headlines have made wild claims about the coming ‘robot apocalypse’, where millions of jobs would be replaced by automation. However, more recently we have seen a more nuanced analysis emerge. This revised analysis focuses on the difference between a task and a job. A task is a discrete segment of work done as part of a worker’s duties of employment, while a job is a bundle of tasks assigned to a worker who performs those tasks and exchanges their labour for pay. A recent report from McKinsey Global Institute estimates that fewer than 5 percent of existing occupations are candidates for full automation. Frequently, rather than completely eliminating jobs, automation and artificial intelligence will replace some tasks, requiring workers to adjust their level of skills and knowledge used in the workplace. In terms of the Canadian context, four separate think tanks estimated the share of tasks susceptible to automation in Canada as ranging between 35 percent and 47 percent. This is across all sectors of our economy. Understanding the Impacts Part of the challenge we identified was how to create an analysis that allows our members to engage in a meaningful way. We created a framework that identifies six general areas of impact – both positive and negative – to make this issue more digestible. The first, and probably most obvious, impact is job loss or displacement, and job estrangement. Job loss or displacement is what most of us think of when we consider tech change at work – ‘I was fired from work and replaced by a robot’. But as we’ve seen, the situation is more complex, and often less dire, than that. Less obvious is what we’ve called job estrangement, where a worker’s role in her workplace changes due to technological change, leaving her feeling alienated from her work, where her skill and knowledge are no longer valued. The second major category of impact is changes in work organisation and required skills. These are impacts that many of us have experienced already. We’ve probably all heard about, and perhaps even participated in ‘up-skilling’, when workers displaced by technological change upgrade their skills to fill new roles that complement and support new technologies. But the...
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.
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 | 0.000 | 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 | 0.000 |
| 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 it