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Automation and Robotics

2025· book-chapter· W7118294646 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in computational intelligence and robotics book series · 2025
Typebook-chapter
Language
FieldEngineering
TopicRobotic Process Automation Applications
Canadian institutionsMacEwan University
Fundersnot available
KeywordsRoboticsAutomationTransformative learningProcess (computing)RobotEmerging technologiesBridge (graph theory)

Abstract

fetched live from OpenAlex

There has been a significant rise in the adoption of emerging technologies. Organizations are now re-evaluating their management practices to ensure they maintain a competitive advantage. While upper-level management is often enthusiastic about the transformative power of technologies such as Robotics Process Automation, lower-level employees face concerns about potential job loss. This chapter emphasizes that, amidst the excitement about automation and robotics, it is crucial for organizations to proactively prepare for the implementation of such technologies. To enhance successful adoption, prioritizing implementation readiness and garnering employee buy-in is vital. This chapter aims to contribute in a trifold manner: 1) Uncover the complex socio-technical processes that shape the adoption of automation and robotics technologies, 2) Emphasize the value of fostering an environment where organizations and employees are racing with and not against emerging technologies. 3) Present best practices to bridge the gap between resistance and readiness for adoption.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.256
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.271
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it