Critique of Sharif’s and Pitt’s Models of Technology

abstract

In this article Pitt’s and Sharif’s models of technology are discussed. These models are based on two different conceptions of technology, which are technology as “instrument” and as “making use of instrument.” Sharif considers technology as a collection of empowering tools, including technoware, humanware, infoware and orgaware. On the other hand, Pitt sees technology as “humanity at work.” Based on his definition, Pitt proposes a model of technology with three components; first-order transformation, second-order transformation, and the assessment of feedback mechanism. In this article this model will be explained and criticized. After that, Sharif’s model is criticized in the light of Pitt’s theory and it will be shown that Pitt’s model provides a better understanding of different aspects of technology. For example, it will be argued how Pitt’s model is efficient in explaining dynamicity, transfer and control of technology along with its soft dimensions, while Sharif’s model is incapable of doing so. In the next part, Pitt’s model is criticized and it is shown that the mechanism of knowledge progress suggested by this model is controversial and Pitt’s framework cannot support the idea of indigenous technology. Furthermore, the ability of Pitt’s model in describing different technological phenomena is called into question, since this model provides a superficial view of the complexity of an assessment of technology’s consequences. Finally, a list is proposed that contains minimal requirements that every model of technology is expected to explain. It is incumbent on technology theoreticians to consider this list.     

Alter Egos: Notes on the basic Processes of specular Identification

Although identity is always dependant on observing minds, identification in physics and biology remains merely external, avoiding therefore the more serious problems of identity which arise in the presence of observed minds. Minds are defined in this paper as specular machines, i.e. systems capable of crossed self‐representations. Specularity is introduced as the primary condition of any objective identification, and self‐identification is presented as the core of specularity. Elementary illustrations are given of the role of specularity in both sociology and psychopathology.     

Multiobjective optimization for interwoven systems

In practical situations, complex systems are often composed of subsystems or subproblems with single or multiple objectives. These subsystems focus on different aspects of the overall system, but they often have strong interactions with each other and they are usually not sequentially ordered or obviously decomposable. Thus, the individual solutions of subproblems do not generally induce a solution for the overall system. Here, we strive to identify “re‐composition architectures” of such “interwoven” systems. Our intention is to connect the subsystems adequately, analyze the resulting performance, model/solve the overall system, and improve the overall solution instead of just solving each subsystem separately. We review recent developments in this field and discuss modeling and solution paradigms in a general and unified framework using the example of an interwoven system consisting of two interacting subsystems.     

Team adaptability and task cohesion as resources to the non-linear dynamics of workload and sickness absenteeism in firefighter teams

ABSTRACT

The current study builds on the non-linear Dynamic Systems (NDS) perspective to test the assumption that change in sickness absenteeism is non-linear, and that such change is due to workload, team adaptability and task cohesion. Participants were 37 firefighter teams (n = 250 individuals) from a main European capital city. The research hypotheses were tested using SPSS and the “cusp” package, in the statistical software R. The results suggest that change in sickness absenteeism behaviours over time is non-linear, with the cusp catastrophe model predicting such behaviours better than the linear and logistic models. In our model, task cohesion functions as an asymmetry factor (i.e., the independent variable that determines the strength and discrepancy between the two stable states of the dependent variable) leading to a linear change in sickness absenteeism. Interestingly, both workload and team adaptability function as bifurcation (i.e., the independent variable that determines the change between the two stable states of the order parameter) and asymmetry factors leading to non-linear and linear change in sickness absenteeism over time. This study contributes to the growing evidence that incorporating the NDS perspective enables a better understanding of action teams, namely those working in extreme environments.     

An introduction to the Priority‐Pointing Procedure

A qualitative research‐based diagnostic procedure, the Priority‐Pointing Procedure (PPP), is proposed that synthesizes responses to open‐ended questions about the direction an organization should take. It points to a priority for action by measuring imbalances in the numbers of responses in the context of the structure of adjustment decision‐making from nomology. It is shown to be useful for defining variables and structuring criteria for strategic Multi‐Criteria Decision Analysis (MCDA) problems. Copyright © 2000 John Wiley & Sons, Ltd.     

An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.