Scientific writing | How to avoid repetitive style caused by an over-use of personal pronouns in the subject position

How to avoid repetitive style caused by an over-use of personal pronouns in the subject position? This question I received a couple of minutes ago at the end of a training session. Avoiding repetitiveness is always important but how to do that when you are not allowed to use the passive voice in scientific publications can be tricky. This sort of thing is especially relevant when scientific journals actively discourage the use of passive voice. How would you know? Well, one thing you could do is to have a look at how other scientists have done this.

Let’s have a look at an example abstract, which is taken from an article published in a scientific journal.

“We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of the same object given many candidate image cut-outs. In addition to image features, we propose utilizing location information about the camera and the object to support image evidence via soft geometric constraints. Our method is compared to existing patch matching methods to prove its edge over state-of-the-art. This takes us one step closer to the ultimate goal of city-wide object mapping from street-level imagery to benefit city administration” (Nassar et al, 2020).

When we look more closely at the words included in the subject-position we see this – see the bold font.

“We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of the same object given many candidate image cut-outs. In addition to image features, we propose utilizing location information about the camera and the object to support image evidence via soft geometric constraints. Our method is compared to existing patch matching methods to prove its edge over state-of-the-art. This takes us one step closer to the ultimate goal of city-wide object mapping from street-level imagery to benefit city administration” (Nassar et al, 2020).

Conclusion

In this abstract we see the following words/parts of speech being used:

  • Personal pronouns: ‘we’
  • Possessive pronouns + nouns: our approach and our method
  • Determiners: This
  • Focus structures: for example, what-clefts. 

So, note that you could use pronouns as well as possessive pronouns, nouns, determiners and what-clefts. The latter is a so-called ‘focus structure’ that should not be over-used as it would lose its communicative effect.

Have you also noticed how linking devices are used here?

Continue learning about related matters

Difficulties labelling Parts of Speech (i.e. building blocks of sentences)? See our video series on the topic called ‘support series 1 and 2‘ on YouTube.

Hope this post has helped clarify things.

Reference:

Nassar, A. S., Lefevre, S. and Wegner, J.D. (2020). Multi-View Instance Matching with Learned Geometric Soft-Constraints. ISPRS Int. J. Geo-Inf.  9(11), 687; https://doi.org/10.3390/ijgi9110687

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